Estèle Glize
email: glize@laas.fr

Marie-José Huguet
email: huguet@laas.fr

Marc Lucas
email: mlucas@groupcls.com

Marion Sutton
email: msutton@groupcls.com

Gilles Trédan
email: tredan@laas.fr

Clustering Sargassum Mats from Earth Observation Data

Keywords: Unsupervised Machine Learning, Clustering, Sargassum Detection, Earth Observation Data

Sargassum seaweed forms large floating mats drifting on the oceans. These mats are increasingly beaching on Caribbean islands, threatening the local wildlife and economies. This paper focuses on their tracking from space, in order to monitor these mats. More specifically, we focus on clustering sargassum mats on satellite images. This constitutes an important building block of the sargassum monitoring system, which then predicts the drift of those clusters to anticipate beachings and warn the local authorities. The difficulty of the clustering operation comes from the noisy nature of input data: image artefacts, partial cloud occlusion, mats discontinuities due to sea conditions, and the difficulty of acquiring ground truth data. This paper details our approach to overcome those challenges. We propose a method (hereafter named Sargassum Mats Detection Method -SMDM) that improves the mats identification by combining a first artefact detection step, a tailored clustering algorithm and a region growing algorithm.

Introduction and Industrial Context

Sargassum seaweed (sargassum fluitans and sargassum natans) has always been present in the Atlantic Ocean. These floating algae have the particularity of forming large mats, sometimes reaching over 10 kilometers in length. These mats drift around the ocean, driven by currents and winds. They are home to a unique ecosystem and are a breeding ground for many marine species. However, when these large mats make landfall, the algae vegetation decomposes, resulting in the atrophication of the nearshore waters and the release of harmful sulfur dioxide gases. The sargassum seaweed invasion came to the fore from 2011 onwards when large amounts of mats started landing on the coastlines of the Caribbean islands and the central American countries. The increasingly heavy impact on the livelihood of the Caribbean societies spurred the scientific community into investigating the phenomena.

Challenges in data exploitation. In 2015, CLS, with the financial support of ESA, started an operational system to provide a variety of users with high quality mat location data using Earth Observation. The project worked on the computation of an improved algae index for each pixel of observation data and on enabling a forecast capability. By using wind and current data, mats movements are estimated to identify the most likely beaching zones. To this end, a drift model is fed with the current location of the mats. Although sargassum mats are easy to spot using the human eye, automating the mats detection is a challenge due to the noisy nature of the observation data. Furthermore, the behaviour of the mats is heavily impacted by environmental conditions as sections of mats might be temporarily submerged in certain wind and wave conditions or not as visible due to the satellite sensor angle.

Contributions. This paper focuses on unsupervised machine learning approach. We propose a method, named Sargassum Mats Detection Method (SMDM), to cluster the distribution of pixels of individual satellite images into a set of sargassum mats.

This paper is organized as follows. Section 2 details the studied problem and reviews some of the state of the art methods to achieve clustering. Our approach, SMDM, is presented in Section 3 and its performances are evaluated in Section 4. Then, we report conclusions of this study and list some perspectives in Section 5.

Problem statement and unsupervised learning

The CLS approach relies on the analysis of satellite images to detect the presence of sargassum mats and to simulate their drift. It is based on 3 steps. First, a data processing step that computes the algae index. Second, a clustering step that detects mats. Finally, a drift simulation step simulates the drifting of detected mats to forecast their stranding.

Data processing. In this paper, we focus on the sargassum mats detection step. Before detailing this step, we briefly present the data that will be used as input. Sargassum presence is detected by the increase of the reflectance spectrum between the red and near infra-red (NIR) wavelengths. The NDVI (Normalized Difference Vegetation Index) has been widely used in the past to map vegetation over land surfaces. Over the ocean, it has been successfully used with MODIS 250-m resolution data to study a severe floating green algae bloom event in the Yellow Sea [START_REF] Hu | Origin and Offshore Extent of Floating Algae in Olympic Sailing Area[END_REF]. Well-known sargassum indices were proposed in the literature, for example the Maximum Chlorophyll Index [START_REF] Gower | Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer[END_REF], the Floating Algae Index (FAI) [5], the Alternative Floating Algae Index [START_REF] Wang | Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations[END_REF], follows the same mathematical statement : AFAI= ρ N IR -ρ N IR where ρ N IR denotes a reflectance, partially (or not) corrected for atmospheric effects in the NIR band, and ρ N IR is the equivalent NIR reflectance that would be measured at the same point in absence of sargassum. ρ N IR is approximated by a linear interpolation between the two reflectances measured at nearby wavelengths in the red and short-infrared bands. We use here a normalized version of the FAI, in which the normalization by the sum of reflectances is introduced to mitigate the variability of the FAI due to atmospheric conditions and observation geometry, as done for the NDVI over land surfaces: NFAI= (ρ N IR -ρ N IR )/(ρ N IR + ρ N IR ). Derivation of floating vegetation presence using NFAI seems thus straightforward. However, a lot of wrong detections are found mostly in the vicinity of clouds. It is thus necessary to implement an automatic editing procedure to remove them. The drawback of such automatic procedure is to also remove true detections. At the end of the data processing step, to each pixel is associated an indicator:

-P to represent the potential presence of algae: P pixel represents the non edited values of NFAI -A to guarantee the presence of algae: A pixel is a pixel with the edited values of NFAI; -S when the pixel corresponds to the sea; -C indicates the presence of clouds or unspecified data. Based on the pixel grid with these indicators, the aim is to find a method to efficiently identify sargassum mats to compute their drift and forecast their strandings. The characteristic shape of these mats is a long and elongated shape, and they are possibly made up in parallel stratum. Even if mats are quite easy to spot using the human eye (especially for oceanographic experts), an automatic detection method faces several obstacles:

1. Mats can be composed of only A pixels, only P pixels, or both. 2. The dataset of images presents artefacts due to multiple scattering in the near infrared occurring in the close vicinity of clouds. These artefacts induce falsely positive values (some of the P pixels). They are characterized by isolated and small groups of P pixels. Other artefacts such as those arising from the satellite angle, the proximity of land as well as the sun glint effect are beyond the scope of this paper. 3. Some C or S pixels may actually divide sargassum mats (with clouds or mats partially submerged). Reconstruction of whole mats by putting together discontinuous clusters should also avoid overestimating the size of mats. 4. Algae density is highly variable, both in different parts of a given image and from one image to another.

Algae contouring. A standard contouring method, the marching squares algorithm (MS), was implemented before this study. MS is a contouring method commonly used, for instance for generating isobars in weather maps. As a generic method, MS is easy to use and requires little parametrization. In MS, only A pixels are used to identify objects that will be associated with sargassum mats. However, A pixels are often disconnected from each other, and this approach produces many contours of small objects. The use of P pixels (or A and P pixels) faces the same limitation: the number of contouring objects is too high and mats appear as multiple independent clusters, which is then problematic for drift simulation. Some sargassum mats can be filtered (for example if they do not contain a sufficient number of A pixels), nevertheless, for the drift step, using such contouring algorithm implies to consider an important set of pixels in input. Figure [START_REF] Bernard | Massive stranding of pelagic sargassum seaweeds onthe french Antilles coasts : Analysis of observed situations with Operational Mercator global oceananalysis and forecast system[END_REF] presents the limitation of MS on A pixels (loss of information) and on both A and P pixels (too many small clusters). Learning methods. Image segmentation methods identify clusters of pixels with similar properties to detect regions or objects into an image. These methods are less sensitive to spatial discontinuity than thresholding methods, and they may overcome the issues of MS. Among image segmentation methods, supervised learning is growing in popularity, particularly for object detection. Convolutional Neural Networks are really effective for visual recognition issues which assign an object to an image [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. For sargassum algae detection, [START_REF] Arellano-Verdejo | ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean[END_REF] develop a neural network, called ERISNet, to determine if a pixel in a satellite image contains sargassum algae. They train their network on labelled data where a pixel is a 1km square of the Mexican coast. This work is an alternative to the NFAI index computation, but doesnt detect sargassum mats. Supervised learning methods require building a set of annotated data (ground truth) to train and validate the neural network. For sargassum mats, there is no ground truth associated with each image. This represents a huge amount of specific work, so we do not select this class of machine learning methods for our sargassum mat detection.

Unsupervised learning methods can group data with similar characteristics from unlabelled data. The interesting features for grouping data are decided upstream by the designer of the algorithm, then the clustering is automatic without outside intervention. For instance, [START_REF] Bernard | Massive stranding of pelagic sargassum seaweeds onthe french Antilles coasts : Analysis of observed situations with Operational Mercator global oceananalysis and forecast system[END_REF] use a k-means method fixing k to 5 to detect sargassum mats. Despite the computational efficiency of the algorithm, fixing the number of sargassum mats upstream in an image doesnt seem appropriate to our problem as the number of mats is unpredictable. Furthermore, this algorithm is noise-sensitive and will affect each P pixel to a mat, even if they are isolated. DBSCAN [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF] is another unsupervised learning method which considers that clusters are connected dense regions in the data space. The algorithm extracts one unvisited random pixel p in the image, and marks it as visited. If p has at least m neighbors within a distance , these neighbors are in the same cluster as p and they are recursively visited to check their ownneighborhood. Otherwise, p is considered noise (without a cluster). This process continues until there is no unvisited pixel in the image. Therefore, DBSCAN requires the setting of two parameters: and m. In our case study, the method has several advantages: the computational efficiency regardless of the size and the shape of the clusters, the identification of noise, the flexibility to the varying number of clusters and the relatively insensitivity to initialization. Because of all the advantages of DBSCAN over other methods, we will use DBSCAN in SMDM presented in the next Section.

Proposed approach

This section presents the processing chain of SMDM to detect sargassum mats on the high seas. The input to this pipeline is the pixel grid, presented in Section 2, containing the indicators P (potential presence of algae), A (algae), S (sea) and C (cloud or unspecified data). SMDM consists of three main steps described hereafter.

Step 1: Remove artefacts. We consider small clusters that do not contain A pixels as artefacts. This step starts with a clustering on the pixels with potential presence of algae (P pixels), and then processes each individual cluster to identify and remove those originating from artefacts. The clustering is performed by DBSCAN on the P pixels with the parameters set to focus the detection on small sized clusters: = 2 et m = 5. This enables the detection of clusters of at least 5 neighboring P pixels. A cluster is then considered as an artefact if it doesn't contain A pixel (guaranteed presence of algae) and if it is sufficiently small (in practice, at most 30 pixels). At the end of this first step, all the presumed artefacts (false positive P pixels) are deleted.

Step 2: Identify Sargassum Clusters. In this step, we identify sargassum clusters in two stages: clusters detection and clusters extension. Detection. The clustering algorithm DBSCAN is used again on the P pixels with slightly relaxed parameters. We set = 2.8 and m = 12 to identify larger clusters than in Step 1. All the identified clusters are given at the extension stage. Extension. As DBSCAN relies on circle-based exploration, elongated clusters (which are quite common) may not be fully detected. To compensate for this effect, we extend the clusters identified in the previous detection phase. Sargassum clusters affected by this step are the continuous and thin ones that are partially detected. For instance, Figure (3) illustrates this problem: while discontinuous mats of circular shape are correctly detected after the identification step (left) and unaffected by the extension (right), elongated and continuous mats are only partly identified by DBSCAN. This partial detection is therefore extended. This stage uses the region growing method [START_REF] Mehnert | An improved seeded region growing algorithm[END_REF]. More precisely, for each cluster G, we inspect each pixel p ∈ G. If p is adjacent to another P or A pixel p that is not in G, we add p to G. We recursively inspect adjacent pixels of p until we find no more adjacent P or A pixel. If the growing of a cluster G leads to add pixels of another identified cluster G, both clusters are merged: G ← G ∪ G. Step 3: Aggregate Clusters into Mats. We extend the clusters from the previous step to find final sargassum mats. Only sufficiently stretched and large clusters are extended. Both criteria were derived from the experts perspective on sargassum mats: large and thin mats. The elongation of a cluster is computed with a Principal Component Analysis on the pixels of this cluster. It gives the direction in which the cluster is the most elongated, as well as the elongation rate on this axis between 0.5 and 1.0. A rate of 0.5 represents a rounded cluster whereas a rate of 1.0 represents a thin and elongated cluster. To extend a cluster, its elongation must be greater than 0.8, and it must consist of at least 50 pixels. The extension of a cluster is made using a slightly modified region growing method to assemble discontinuous clusters. Therefore, the clusters affected by this step are those partially separated by a few pixels of sea or clouds (C or S pixels). More precisely, for a cluster G, we consider each pixel p ∈ G, and we inspect each pixel p at 2 pixels from distance to p. If p is a P , A or C pixel and is at less than 5 pixels from the direction of elongation of G, we add p to CL. Then, we recursively inspect adjacent pixels of p in the direction of elongation of G until we find only S pixels. If the growing of a cluster G leads to add pixels of another identified cluster G, both clusters are merged: G ← G ∪ G.

Algorithm Output and Post-processing. A post-processing stage allows to attribute a confidence level to sargassum mats. We have defined three confidence levels sorted by decreasing likelihood. A pixel in a mat of a level is necessarily in a mat of an upper level.

-Level 1 regroups the sargassum clusters found at the end of Step 2 containing at least one A pixel. -Level 2 considers all sargassum mats found at the end of Step 3, but it removes all pixels located less than 10 pixels from a C pixel and which are not in a mat of the confidence level 1. In this level, a mat with no A pixel can appear, but it removes the uncertainty of algae near clouds. -Level 3 regroups all sargassum mats found at the end of Step 3. In the confidence level 3, a mat with no A pixel can appear, and mats are more likely to cross clouds or sea.

Evaluation

In this section, we present our approaches to validate our solution. The major difficulty of this validation is the absence of ground truth data against which we could quantitatively compare. We circumvent this problem by relying on two complementary approaches. First, we rely on the qualitative feedback from oceanography experts that were involved throughout the process. Second, we compare against MS, which constitutes the baseline used in production over some metrics capturing the size and the elongation of identified clusters.

Expert Input. Since there exists no ground truth regarding sargassum mats, validation of the previous approaches has been conducted using expert input. More precisely, oceanographers leveraging both their sargassum and earth observation knowledge are presented with a visualisation of the detected mats. Figure [START_REF] Gower | Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer[END_REF] presents the results of our solution as presented to the experts. While some artefacts remain (e.g. East of both maps), the detection avoided the majority (e.g. Center of Map1). Moreover, SMDM manages to identify clear elongated mats (e.g. North of Map2), even in challenging conditions like partial cloud occlusions (e.g. South-West are of Map1).

Comparing against Marching Squares. Prior to the deployment of our solution, sargassums mats were clustered using the MS algorithm. Because of its historical position and the absence of other competitor, MS is considered as the baseline hereafter. Since MS does not produce confidence levels, to ensure a fair comparison we set the "High confidence Level" of MS as the result of MS on A pixels only, and the "Low confidence Level" as the result of MS on P pixels. Similarly, we focus on the two highest confidence levels of SMDM.

Table 1 compares both approaches on Maps 1 and 2. Recall that Map 1 is paradigmatic of an occluded region containing many clouds and many artefacts, whereas Map 2 represents a good situation with few clouds and clear mats. First, SMDM is slightly slower than MS on A pixels. This originates from the processing time for steps 1 and 3 in SMDM. On the bright side, SMDM generates all confidence levels within this time, whereas MS, when run on P pixels takes considerably longer. This gap in MS performance is explained by the number of clusters it identifies. For instance, on low confidence level in Map 1, MS identifies around 80 times more clusters than SMDM. The identified clusters are all very small. In contrast, SMDM identifies a small number of large clusters, even at the high confidence level. Overall, those results shows SMDM meets its objectives: identify less clusters of bigger size. While interacting with the experts and exploring the results of both methods, we used 6 additional maps that are not presented here as they confirm the above conclusions without bringing further insights.

Conclusion

To sum up, this paper presents SMDM, an unsupervised clustering approach for Sargassum mats detection. It consists in a sequence of steps to i/ detect and remove artefacts from the data ii/ cluster the cleaned data conservatively and iii/ grow the clusters into mats by exploiting the specificities of their geometry. This approach is then compared against current SoA method, and its benefits are illustrated on two test maps. Dealing with the absence of ground truth data has been the central challenge of Sargassum detection, both for the design step (restricting the approach to unsupervised methods) and for the validation step. Hence a first promising research direction is to explore the temporal dimension of the problem. Since two consecutive images should contain reasonably similar geographic distributions of mats, exploiting these similarities could make up for a ground truth proxy. However, tracking a sargassum mat across multiple shots is not trivial due to drifts, cloud movement and varying satellite angles. Driven by the growing number of freely accessible Earth Observation images, a second research direction consists in applying SMDM to other oceanographic features, in particular river plumes and chlorophyll filaments.

  Throughout this paper, we will illustrate the different steps with two satellite images (part of the Caribbean Sea) taken from MODIS 250-m sensor: Map1 (long: [-60.8,-58.8], lat:[15.2,16.7], day: 10/07/19) and Map2 (long: [-63.2,-61.2], lat: [13.9,15.4], day: 10/07/19) These images are grids of [801 601] pixels. The Figure (1) gives an illustration of Map1 with indicator on each pixel.

Fig. 1 :

 1 Fig. 1: Map1-Input of SMDM. Pixels A in Yellow; P in Red; S in Blue; C in White.

Fig. 2 :

 2 Fig. 2: Illustration of MS on Map1. The convex hull of sargassum mats are in light blue.

  (a) After detection. (b) After detection and extension.

Fig. 3 :

 3 Fig. 3: Illustration of Step 2. The convex hull of sargassum clusters are in light blue.

Fig. 4 :

 4 Fig. 4: Example output on Maps 1 & 2. Detected mats are circled in light blue.

Figure ( 5

 5 ) sketches an exploratory analysis of the identified clusters. The left part represents the distribution of cluster sizes identified on Maps 1 and 2 for both methods. It confirms the previous results: the vast majority of the many clusters identified by MS are below 100 pixels, whereas the SMDM clusters are nearly always above 100 pixels. A careful comparison of SMDM in Low and High confidence levels allow to observe that SMDM traded some 500 High confidence clusters for fewer large (> 2000 pixels) Low confidence clusters.

This work was funded by the CNES (Centre National des Etudes Spatiales)

&

The number of clusters is limited to 100 for readability. Maximum number of clusters for MS is over 900 in the Low Confidence level. The layout of the largest clusters is for high confidence clusters on Map 1.

Acknowledgements The authors would like to thank Jacques Stum for kindly explaining the data processing step and the NFAI index computation.


Anastasia Bolotnikova

Sébastien Courtois

Abderrahmane Kheddar

Autonomous Initiation of Human Physical Assistance by a Humanoid

We study the use of humanoid robot technology for physical assistance in motion for a frail person. A careful design of a whole-body controller for a humanoid robot needs to be developed in order to ensure efficient, intuitive and secure interaction between humanoid-assistant and human-patient.

Here, we present a design and implementation of a wholebody controller that enables a humanoid robot with a mobile base to autonomously reach a person, perform audiovisual communication of intent, and establish several physical contacts for initiating physical assistance. Our controller uses (i) visual human perception as a feedback for navigation and (ii) joint residual signal based contact detection for closed-loop physical contact creation. We assess the developed controller on a healthy subject and report on the experiments achieved and the results.

I. INTRODUCTION

According to the most recent predictions, by the year of 2050, 1 out of every 6 people in the world will be over the age of 65, compared to 1 out of every 11 for the same age group in 2019 [START_REF]World population prospects 2019. highlights[END_REF], [START_REF]World population ageing 2019. highlights[END_REF]. As a result, demand for healthcare professionals, who can help frail elderly with everyday routine tasks, is expected to increase dramatically. With a shortage of people of working age, who could satisfy this demand, robotization of the caregiving operating industry is required. Moreover, the recent Covid19 outbreak brought to light the crucial role that robots can play in future pandemics.

Robots are expected to play a pivotal role in the future of healthcare for elderly and disabled, improving both quality of life of patients and quality of work of human caregivers [START_REF] Niemelä | Robots as social and physical assistants in elderly care[END_REF]. Particularly, user-friendly humanoid robots [START_REF] Pandey | A mass-produced sociable humanoid robot: pepper: the first machine of its kind[END_REF] may be a good fit for the job thanks to their familiar shape [START_REF] Staffa | Recommender interfaces: the more humanlike, the more humans like[END_REF], potential multi-functionality and natural communication capabilities of a social robot specifically designed to interact with people in a natural, intuitive and useful way [START_REF] Daily | Affective computing: historical foundations, current applications, and future trends[END_REF].

In this work, we consider a general interaction scenario which may occur in everyday care. A humanoid robot is required to autonomously reach a person and establish several physical contacts on a human body to initiate physical assistance in motion. A whole-body quadratic programming (QP) controller framework 1 is used to develop a Finite State Machine (FSM) controller that enables a humanoid robot to autonomously participate in such interaction scenario.

A visual human perception is used for closed loop navigation towards a human. The joint residual signal based contact detection is used to determine time of robot-human contact events [START_REF] Bolotnikova | Contact observer for humanoid robot pepper based on tracking joint position discrepancies[END_REF], [START_REF] Bolotnikova | Compliant robot motion regulated via proprioceptive sensor based contact observer[END_REF]. Verbal, visual and body language 1 SoftBank Robotics Europe, Paris, France2 University of Montpellier-CNRS LIRMM, Interactive Digital Humans, Montpellier, France 1 https://jrl-umi3218.github.io/mc_rtc communication is included in the controller design to enable a robot to autonomously communicate its intentions to a human. More specifically, our contributions are following:

• the design of a whole-body controller architecture for the interaction scenario (Sec. III-A); • the implementation of a visual feedback based navigation towards a human (Sec. III-B); • the integration of a multi-modal communication of intent in the controller design (Sec. III-C); • finally, we present the results of using the controller for an interaction trial with a healthy subject (Sec. IV).

II. BACKGROUND

In the field of research on physical human-robot interaction (pHRI) for assistance in motion or power augmentation, a large majority of the works consider either the scenarios where human is creating a contact on the robot body surface [START_REF] Tirupachuri | Trajectory advancement during human-robot collaboration[END_REF], [START_REF] Romano | The codyco project achievements and beyond: Toward human aware whole-body controllers for physical human robot interaction[END_REF] or the application of exoskeletons [START_REF] Vaca Benitez | Exoskeleton technology in rehabilitation: Towards an emg-based orthosis system for upper limb neuromotor rehabilitation[END_REF]. In our work, however, the roles are reversed, it is the robot who is responsible for autonomously establishing a physical contact. An interaction scenario where the robot itself is actively and autonomously creating contacts on a surface of a human participant body is rarely considered. In [START_REF] Yamada | Humanrobot contact in the safeguarding space[END_REF] a control of a robot establishing a contact on the human body was studied with consideration of safety limits in terms of the human pain tolerance limits. In [START_REF] López | Compliant control of a humanoid robot helping a person stand up from a seated position[END_REF] a humanoid robot was used in a sit-to-stand assistance context to contact a human and perform a motion while maintaining the contact.

Human-aware navigation is a basic skill that a robot must have when operating in the same environment as humans [START_REF] Kruse | Human-aware robot navigation: A survey[END_REF]. A sensor fusion navigation approach was developed and tested in [START_REF] Kit Tsun | An improved indoor robot human-following navigation model using depth camera, active ir marker and proximity sensors fusion[END_REF]. More often human avoidance strategies in navigation are studied [START_REF] Lam | Human-centered robot navigation-towards a harmoniously human-robot coexisting environment[END_REF], [START_REF] Araujo | An architecture for navigation of service robots in human-populated office-like environments[END_REF]. Visual servoing was used to make a robot navigate towards a human, maintain constant distance between itself and a human, and follow a walking human. This visual servoing based control was presented in [START_REF] Claudio | Vision-based manipulation with the humanoid robot romeo[END_REF], [START_REF] Agravante | Visual servoing in an optimization framework for the whole-body control of humanoid robots[END_REF]. Although, this work does not include the details of the navigation method. However, a demo of this navigation approach is available online 2 .

Yet, none of the previous works have considered a full autonomous interaction scenario with integration of all components of visual feedback based navigation towards a person, multi-modal communication of intent and closed-loop physical contact creation. In our work, we describe the design and implementation of the whole-body QP FSM controller for the studied pHRI scenario (Sec. III) and present the experimental results (Sec. IV).

III. CONTROLLER ARCHITECTURE

The whole-body controller is developed using mc rtc QP task-space control framework. We consider a complex interaction scenario which is comprised of several distinct stages, each of which consists in achieving multiple objectives. Therefore, the controller is implemented in a form of an FSM, where every state corresponds to a specific stage of the interaction process. Fig. 1 presents the general structure of the implemented FSM controller. In Sec. III-A we describe this controller implementation in detail.

A. FSM QP controller implementation

The robot actions are controlled by an accelerationresolved QP controller. This means that an optimization problem is formulated with a quadratic objective function that consists of a weighted sum of tasks formulated as errors between actual and desired setpoints in task space, as well as first and second derivatives of these task errors. Robot joints acceleration are the decision variables of the problem. A set of linear constraints in the optimization problem formulation ensure that the solution is physically feasible and safe [START_REF] Bouyarmane | Quadratic programming for multirobot and task-space force control[END_REF].

As indicated in Fig. 1, the interaction starts with controller initialization (init). At this stage, the robot description module is used to build the base control problem as QP with default tasks and constraints (Eq. 1).

min q,f P + B + C (1a) s. t.         
joint position, velocity and torque limits self-collision avoidance sliding ground contact constraints bound mobile base velocity and acceleration

(1b) (1c) (1d) (1e)
The objective function of the QP contains a default posture task P, default mobile base position task B and a center of mass task C. Interested reader can refer to [START_REF] Bouyarmane | Quadratic programming for multirobot and task-space force control[END_REF] for detailed definition of these common QP tasks and constraints.

After the controller initialization, transition to InitialPosture state is triggered. At this stage, we assume that the robot is positioned far from and facing the human participant. In this state, the default posture task P is given a new setpoint q d , which is an upright standing posture. We define a threshold δ P and consider this state to be completed when the posture task error P is less than or equal to this threshold, which triggers transition to the NavigateToHuman state.

In the NavigateToHuman state, default mobile base task B is removed from the problem formulation and a Position Based Visual Servoing (PBVS) task V, regulating mobile base motion, is added. The objective of this task is to minimize the error in the camera frame between mobile base position, computed from kinematics tree, and target mobile base position, which is defined w.r.t human frame detected in robot camera field of view (Fig. 2). We detail how this target position is computed and reached in closed-loop in Sec. III

≤ δ P B ≤ δ B t i I > T i I headOrientationOK i E ≤ δ i E Contact detected, i < N ; i = i + 1 i = N
Fig. 1: FSM based controller design for interaction scenario. Now, the robot executes a sequence of predefined contact tasks T c i , i = 0, . . . , N . For every contact task, the first state (of the sub-FSM) is IntentCommunication, where the robot explains and illustrates its further intentions specific to T c i to the human (Fig. 3). This state is implemented to ensure smooth and comprehensive transition to the states where the robot establishes physical contacts with the human. We detail the purpose and implementation of this state in Sec. III-C. Human head orientation is monitored in this state to verify that a human has paid attention to the visual communication on the robot screen. The time for intent communication is predefined T I . Once state time t I exceeds T I intent is considered to be successfully communicated and transition to the PreContact state is triggered. After robot prepares for a contact, appropriate joint residual signal [START_REF] Bolotnikova | Contact observer for humanoid robot pepper based on tracking joint position discrepancies[END_REF], [START_REF] Bolotnikova | Compliant robot motion regulated via proprioceptive sensor based contact observer[END_REF] is monitored in the MakeContact state, as robot is moving its end-effector towards the human body, to detect the time of the contact event. When all contact tasks are finished the experiment ends with RemoveContacts state and moving away.

B. Nearby surrounding navigation towards human

We define a fixed mobile base position and orientation target w.r.t human reference frame h X * b . The human reference frame (Pelvis link) pose in the robot camera frame is obtained from human Body Tracking by Azure Kinect camera installed on the robot, we denote this transformation as c X h . From the joint encoder readings and known robot kinematics we compute transformation between camera frame and mobile base frame b X c , which at the start of the experiment is equal to the world frame b X c = w X c at t = 0, where t is the time elapsed from the start of the experiment. The desired setpoint pose h X * b in the camera frame is (Eq. 2)

c X * b = c X h h X * b (2)
All frames involved in these computations are illustrated on the experiment simulation setup in Fig. 2. This update is performed on every iteration of the controller and passed as a target to the PBVS QP task V associated with the motion of the mobile base. Due to the fact that a camera and the body tracking frame rate ( 30Hz and 15Hz respectively) is much lower than the control frame rate ( 83Hz in case of Pepper), new detection data is not available for every controller iteration. This may result in a sudden task error jumps and discontinuities. For a smoother PBVS task error evolution and convergence, in this work, we set lower limits for the mobile base speed and acceleration.

The error between the current and desired mobile base frame poses is used as a feedback error for the PBVS task V to navigate in closed-loop to the desired position (Eq. 3)

B = c X b ( c X b * ) -1 (3)
Detected human head frame is used for a camera orientation target task implemented in the controller as an Image Based Visual Servoing (IBVS) task O. With head X c being a human head frame pose in the camera frame detected by Azure Kinect, the O task error which ensures that human head is kept as close as possible to the center of the field of view, (FoV) is O = head X c .translation Based on our experience, keeping a human head in the center of FoV results in a better overall human body tracking results from the Azure Kinect, especially once some human body parts become occluded and are thus no longer in the FoV.

In the future, we consider combining this method with V-SLAM 3 technology for highly robust performance. This is necessary for improved safety and for handling low frame rate and high frame latency of Azure Kinect depth sensor. 3 We choose SLAM as it will certainly be a mature technology to navigate inside rooms indoor senior citizens' homes, personal houses, hospitals, etc.

C. Intent communication for user comfort and safety

Humans usually express interest in touching a humanoid robot when they see one. However, when the roles are reversed and it is the robot that will establish physical contacts on a human body, the loss of control from a human side, the lack of understanding of the robot intentions, and the low communicative cognitive capabilities of the robot can cause discomfort and even fear during the interaction. The closer a robot is to a person, the more considerations need to be taken into account for human comfort and safety. And in the case of direct physical contact, human participant safety and comfort (both physical and mental) are of extreme importance for successful human-robot interaction.

Indeed, a human participant needs to trust the robot to feel comfortable to allow it to establish physical contact. And this trust can be established only if the human can predict what the robot intentions are. Therefore, we integrate good user experience design considerations as parts of the FSM controller in order for the human to feel comfortable while the robot is in close proximity and is establishing physical contacts with the human. For that robot is programmed to clearly communicate its intentions to the user, using different communication modalities (verbal, visual, body language).

When the robot reaches the person close enough, prior to establishing physical contacts, the robot is communicating verbally what it intends to do next. At the same time, using body language the robot draws the attention of the human participant to its tablet screen where an illustration of the intended physical interaction is displayed on a schematic figure, as is shown in Fig. 3.

With these familiar tools for information transfer, we enrich the interaction with different communication modalities and enable the robot to make its intentions very clear prior to establishing physical contact. The physical contacts are established using posture QP tasks to move robot's right or left hand links towards the surface of the uman body where the contact is supposed to be established (Fig. 4). Contacts are established in closedloop via monitoring of residual signal between predicted (learned) and measured joint position tracking errors [START_REF] Bolotnikova | Contact observer for humanoid robot pepper based on tracking joint position discrepancies[END_REF], [START_REF] Bolotnikova | Compliant robot motion regulated via proprioceptive sensor based contact observer[END_REF]. Predefined contact locations on a human body in this work were inspired from observing the human caregiver's practices for similar interaction tasks. The exact position of the contacts can be adjusted to different human morphologies or chair heights. However, the feasible contact locations are limited by the robot's reachable workspace. The exact feasible placement of the contacts can be planned by analyzing the human point cloud using the method presented recently [START_REF] Bolotnikova | Multi-contact planning on humans for physical assistance by humanoid[END_REF].

IV. EXPERIMENTAL RESULTS

We have achieved preliminary experiments that will be enhanced further in the future work. 4

A. Platform description

We are using the upgraded prototype of Pepper wheeled humanoid robot platform that has been customized for later in-situ experiments with real patients. With respect to the commercial version, this robot has an additional Azure Kinect camera mounted on the top of the head, mainly for human body tracking. The robot is also equipped with the RealSense D434 camera for SLAM and additional source of IMU measurement that we are not using in this paper. These additional hardware elements are added to the robot description module and used by mc rtc framework to build the base controller QP formulation. Under the tablet, this Pepper version has embedded Jetson TX2 for image processing and other heavy computation. For the time being, we are running the controller on an external PC for easy debugging and programming purposes.

B. Results

At the start of the preliminary experiment, the robot is placed about 1.2 meters away from a chair where the human participant is sitting. First, the robot navigates towards the person, then communicates its intentions, and proceeds to initiate several physical contacts. First, robot establishes a contact with the right shoulder of the human participant. Then, the robot invites the participant to place their hand in the robot's right end-effector (as is being simultaneously demonstrated on the tablet screen of the robot). The experiment ends with all contacts being autonomously removed, robot thanking the participant and moving away.

Fig. 6 shows main stages of the experiment. Interested reader is invited to see the video accompanying this paper for the full experiment demonstration or to refer to the controller source code for implementation details 5 .

Plot on Fig. 5 shows evolution of the PBVS task errors in XY translational and Z rotational axes during the Navi-gateToHuman state (Fig. 6a). The task error evolution and convergence is mostly smooth. A slight sudden jump occurs at the end of the state for the Y axis, when the robot is very close to the human participant. Typically, at this stage the depth perception deteriorates due to the human being too close to the camera and, as a result, discontinuities in human body tracking are also likely to occur. Nevertheless, the NavigateToHuman state completion criteria is successfully reached and transition to the next FSM state is triggered. The orientation of the human head frame is monitored, and the robot checks if the human face was oriented to look at the robot's tablet at least once for paying attention to the figure displayed on the screen. This is an additional criteria for exiting IntentCommunication state. The plot of the monitored human head to robot tablet angle is shown on Fig. 7. This plot validates that, shortly after the start of IntentCommunication state, the human head is being oriented to look at the tablet. Once intent is communicated, the transition to PreContact and then MakeContact states for the robot's left end-effector are triggered. The robot starts to move its left arm towards human back to establishes a physical contact on the right shoulder of the participant (Fig. 6c). The monitoring of the residual between measured left shoulder joint position tracking error and predicted expected position tracking error of this joint allows to detect the contact event (as mentioned in Sec. III-D). Fig. 8 shows the residual signal for the left robot shoulder roll joint. As can be seen from the plot, the contact is detected (when residual reached a threshold) and maintained for several seconds after the detection occurs as is requested according to the MakeContact state design. Fig. 8: Left end-effector contact detection Similar process is repeated for the robot's right endeffector. However, in this case, the robot invites the user participant to place their hand into the robot's right end effector, which is brought up in front of the user as is shown in (Fig. 6d). The plot on the Fig. 9 shows the residual signal for the robot right elbow roll joint and indicates successful detection of the contact event. Once the contact is detected, the robot closes its right gripper slightly, as a human would do in a similar interaction. This contact is also maintained for a few seconds before being removed.

The experiment ends with the robot autonomously and carefully removing all established physical contact one by one. The robot thanks the human user for participation in the experiment and moves away. In this paper, we integrated and enhanced different bricks to build an architecture that enables the humanoid robot Pepper to reach a person in need for physical assistance, communicate its intentions and establish several physical contacts to initiate the assistance process. We developed a taskspace optimization controller instance derived from [START_REF] Bouyarmane | Quadratic programming for multirobot and task-space force control[END_REF], enhanced with visual servoing [START_REF] Paolillo | Interlinked visual tracking and robotic manipulation of articulated objects[END_REF], which allows reaching safely a person. In close contact interaction the robot initiates contacts [START_REF] Bolotnikova | Contact observer for humanoid robot pepper based on tracking joint position discrepancies[END_REF] to achieve compliant contact motion [START_REF] Bolotnikova | Compliant robot motion regulated via proprioceptive sensor based contact observer[END_REF]. The controller was enhanced with communication plug-ins.

We believe that all the bricks that have been developed are viable ground technologies to effectively achieve frail pHRI purpose in real experiments. The latter are planned to be conducted in-situ a retirement house with few real patients in our future work; where participant impression during the interaction will also be evaluated. Engineering efforts are now undertaken to clean the existing software to meet safety requirements and recovery procedures. SLAM technology will certainly mature to a real-time reliable and commercially available software and is integrated (but not used in this paper) to navigate in indoor spaces. We forecast the same maturation for human body tracking. Task-space optimization have been proved extremely flexible and reliable in many industrial set-up. In near future work, the human model will be integrated as part of the multi-robot QP control model [START_REF] Bouyarmane | Quadratic programming for multirobot and task-space force control[END_REF]. Thereby, we are synthesizing an adaptive force controller in the task space to allow the robot to effectively assist the site-to-stand motion, human limbs manipulation, etc. The latter problem is challenging because a human is not equipped with encoders or torque sensing which would allow closing the loop and computing the right amount of assistive forces. We will also redesign the balance criteria for the humanoid to increase safety of the dyad during pHRI.

Fig. 2 :

 2 Fig. 2: NavigateToHuman FSM state visual tasks

Fig. 3 :

 3 Fig. 3: Multi-modal IntentCommunication FSM state

Fig. 5 :

 5 Fig. 5: Evolution of navigation task errors

Fig. 6 :

 6 Fig. 6: Robot onboard camera view and video screenshots illustrating main parts of the experiment with human participant

Fig. 7 :

 7 Fig. 7: Human face to tablet angle

Fig. 9 :

 9 Fig. 9: Right end-effector contact detection

https://youtu.be/QDmDY5koKIE

For the time being, the experiments with more participants couldn't be achieved because of the restrictions of the Covid19 outbreak.

https://github.com/anastasiabolotnikova/autonomous_ phri_init


Dr Maxime Rémond

Jongun Hwang

Jinbo Kim

Prof. Dr Donghwan Kim

Dr Christophe Bucher

Dr Yann Bretonnière

Saeon Kim

Dr Chantal Andraud

Prof. Dr Eunkyoung Kim
email: eunkim@yonsei.kr

Push-Pull Dyes for Yellow to NIR Emitting Electrochemical Cells

Keywords: organic light-emitting electrochemical cells, fluorescent materials, near-infrared, host/guest

come

Introduction

Light-emitting cells (LECs) are electrochemical devices composed of a mixture of an emitting dye and an electrolyte sandwiched between two electrodes. Application of a voltage results in redistribution of the ions, followed by electrochemical doping of the active layer by injected charges and thus the in situ generation of a p-n junction within the emitting layer. [1,2] Ultimately, recombination of the injected holes and electrons in excitons enables electroluminescence (EL) from the emissive material. Compared to well-known organic lightemitting diodes (OLEDs), these LECs have unique advantages, such as simple device structures and a low driving voltage, and the possibility of using air-stable electrodes as a result of this electrochemical doping process. [3,4] However, this doping also leads to several drawbacks, such as a longer turn-on time and lower efficiency, that will need to be addressed before practical application. [5] Most LEC devices are based on a fluorescent conjugated polymer (CP) or an ionic transition metal complex (iTMC) as the emitting material. [6][7][8][9] Over the last decade, these two types of materials have yielded LEC devices with exceptional brightness. [10][11][12] However, CPs suffer from low purity levels that can generate unwanted side reactions, and iTMCs suffer from the scarcity and high cost of iridium and ruthenium complexes. Recently, Edman et al.

demonstrated that small molecules could undergo the same peculiar LEC mechanism and could be used as alternative light-emitting materials for LECs. [13] Since then, many families of classical organic dyes, such as perylenes, [14] cyanines, [15] porphyrins [16,17] and thermally activated delayed fluorescence (TADF) dyes, [18,19] have been incorporated into the emissive layer. [20] Similar to CP-and iTMC-based LECs, the performance of LECs using small molecules is highly dependent on interchromophore interactions in thin films. As in organic light-emitting diodes, the use of solid-state emitters or the design of multicomponent host/guest systems are possible ways to minimize exciton quenching in the active zone of LECs with small molecules. [10,13,21] Nonetheless, the LEC performance of typical organic chromophores is still quite low compared to that of CP and iTMC counterparts. In this context, the design of small, metal-free dyes for LEC devices is a challenging task, especially in the near-infrared (NIR) region, where the low bandgap usually leads to low luminescence and EL efficiencies. [22][23][24][25][26][27][28] Among the organic chromophores, push-pull (D-π-A) dyes present unique intramolecular charge transfer (ICT) properties, which can be fine-tuned by changing the donor (D), acceptor (A), and πbridge. During the ICT process, significant polarization changes in the whole πsystem of the dye control not only the optical properties but also intermolecular interactions, which are critical for the modulation of EL properties. [29] In this context, D-π-A molecules could become an important class of light-emitting materials due to their dipolar charge transfer nature, capability of band-gap and luminescence optimization through molecular engineering and spectral tunability in far-red to NIR emission. [30,31] Herein, we present push-pull dyes with a diphenylamine (DP) or carbazole (Cz) as donor, and indandione and 2,5-dihydrofuran as acceptor groups. Fluorenyl (F) or an unsubstituted ethene derivative (E) was introduced between the donor and acceptor as a πbridge. These dyes feature high thermal stabilities and solution processibility for film formation with the distinct CT nature of the push-pull dyes. Host materials for these dyes were explored by tuning their energy levels and spectral overlaps. A low-voltage working organic LEC could be obtained for dye I with a peak irradiance of 425 µW/cm 2 . In addition, NIR LECs emitting at 825 nm and 800 nm were obtained with peak irradiances of 19 µW/cm 2 and 123 µW/cm 2 from dye III in the 1,3-bis[2-(4-tert-butylphenyl)-1,3,4-oxadiazo-5yl]benzene (OXD-7) and 2,5-bis(5-tert-butyl-benzoxazol-2-yl)thiophene (BBOT) hosts, respectively. These low-cost NIR LECs would be particularly interesting for lightweight military, medical or security authentication technologies. [32]

Results and Discussion

Optical and electrochemical properties of push-pull dyes

The five push-pull dyes (Figure 1a) show yellow to NIR emission and diverse optical and electrochemical properties. DPF dyes (I-III) undergo ICT though a fluorene π-bridge between diphenylamine and an acceptor group: 1,3-indandione (I), 2-(dicyanomethylene)-3phenylthio-4,5,5-trimethyl-2,5-dihydro-furan (II) and 2-(dicyanomethylene)-3phenylsulfonyl-4,5,5-trimethyl-2,5-dihydro-furan (III). [30,31] Dye IV contains N-(2methoxyphenyl)carbazole as the donor and the same acceptor as II. Finally, dye V contains N-ethylcarbazole as a donor and 4,5,5'-trimethyl-3-(phenylsulfonyl)furan-2(5H)-one as an acceptor. [33] Dye IV was synthesized by Knoevenagel condensation between 4-(9H-carbazol-9-yl)-3-methoxybenzaldehyde and 2-(dicyanomethylene)-3-phenylthio-4,5,5-trimethyl-2,5dihydro-furan under microwave irradiation (Scheme S1). The synthesis of the other dyes, also by Knoevenagel condensations, was previously described. [30,31,33] .

The thermal properties of the dyes showed high melting points (> 225 °C) as well as excellent thermal stability (> 290 °C), as determined by differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA) (Figure S2 and Table S1). In particular, dyes I and II showed the best thermal stability and the highest thermal degradation temperature for 5 % weight loss (T 95% , ~ 340 °C). Dye III showed the lowest T 95% , but this value was still high (290 °C). Thus, dyes I-V feature high thermal stabilities, well above the typical annealing temperatures used during the film forming process or the operation temperatures.

Finally, glass transitions (T g ) were observed for dyes I (80 °C), II (100 °C), III (125 °C) and V (95 °C). Emitters with a high T g are of particular interest for light-emitting devices because they are able to form stable amorphous films.

The optical properties of the dyes in chloroform and crystalline powder are represented in Figure 1b and Figure S3. The DPF dyes (I-III) are strongly fluorescent in the far-red to NIR region in dilute apolar solutions (674-788 nm in chloroform), with high photoluminescence quantum yields (PLQYs) of 51-30 %, arising from the rigid fluorene bridge. The lifetimes of the dyes in chlorobenzene are in the nanosecond range (Figure S3b).

Dye I showed the longest lifetime, while III showed the fastest decay among the DFP dyes, as listed in Table 1. Importantly, those dyes are emissive in the solid-state, notably in crystalline powder, due to the nonplanar DP units. In particular, dyes I and II produce strong solid-state fluorescence with PLQYs of 22 % and 28 %, respectively, in powder. On the other hand, due to its small bandgap, the powder of III exhibits a lower PLQY of 9 % (Table 1).

However, this value is still remarkable considering the emission in the NIR region (emission maximum of 800 nm).

In comparison, the optical properties of CzE dyes (IV and V) are blue-shifted, as expected from the lower donating capacity of carbazole (Cz) and their shorter π-bridge (E). In dilute chloroform solution, these two dyes are moderately (11 %) and weakly (< 1 %) emissive, respectively. The reduced PLQY of these dyes in solution may be attributed to enhanced intramolecular motions and the formation of a twisted configuration aound the πbridge in solution, which is characteristic of AIEE dyes. Nevertheless, their crystalline powders are strongly emissive in the orange/red region, with PLQYs of 45 % at 640 nm and 33 % at 600 nm, respectively.

All dyes in this study showed strong solvatochromism due to their strong push-pull character (Figure S4 for dyes IV and V; see also ref for I-III, [30] [31] [31] ). The Lippert-Mataga model was used to fit the Stokes shifts of the dyes to the solvent orientation polarizability (f)

(Figure S5, Table S2). [34,35] The Onsager radii were estimated from Suppan's equation [36] (see SI) using the density obtained from X-ray diffraction on single crystals. [31,33,37] From those Onsager radii (Table S2), dipole moment variations from 16~19.1 (I~ IV) to 9.6 D (V) were determined. Thus, the dipole moment increase upon excitation of dyes I-IV is approximately twice as strong as that of dye V. Finally, it is interesting to note that all dyes present large Stokes shifts (5000-7500 cm -1 ), also characteristic of their strong intramolecular CT, so low auto-absorption can be expected in the films.

The HOMO and LUMO levels of the dyes were estimated from cyclic voltammetry (CV) measurements conducted at 100 mV/s in degassed dichloromethane solutions using tetra-n-butylammonium perchlorate (TBAP) as supporting electrolyte (Figure 1c). The CV curves recorded at a vitreous carbon electrode revealed that the DPF dyes (I ~ III) are reversibly oxidized at similar oxidation peak potential values (0.67 V, 0.65 V and 0.67 V, respectively), which is consistent with a triarylamine-centered oxidation (N(Ar) 3  N + (Ar) 3 ).

Conversely, these compounds were found to be irreversibly reduced over a quite broad potential range, i.e., from -0.75 to -1.5 V, with potential shifts varying with the electronwithdrawing degree (EWD) of the substituents introduced on the acceptor part. As shown in Figure 1c, increasing the strength of the EWD results in a large shift of the reduction potential towards more positive values, i.e., from E peak =-1.33 V for dye I and -1.20 V for dye II to -0.92 V for dye III. The HOMO and LUMO energies (Table S1) determined from CV suggest that the DPF dyes have similar HOMO energy levels, approximately -5.2 eV, while their LUMO levels decrease with the EWD of the substituents, from -3.36 eV (I) to -3.75 eV (III). The red-shift of absorption and emission for dyes I~III were well matched to the CV results.

The CV curves of the CzE dyes (Figure 1c) reveal that dyes IV and V are irreversibly oxidized at higher potential values than the DPF dyes, which is consistent with the fact that carbazoles are weaker donors than DP. It should be noted that dyes II and IV, featuring an identical acceptor group, are reduced at similar potential values, i.e., at -1.20 V and -1.14 V, respectively. The HOMO energies of dyes IV and V are approximately -5.6 eV, and the LUMO energies are -3.52 eV and -3.27 eV, respectively. Both electrochemical and optical bandgaps (Table S1) show the same tendency with a bandgap decrease in the order of dye V>

IV > I > II > III.
Geometry optimizations of the ground state of the dyes in vacuum were carried out using density functional theory (DFT) with the Gaussian 09 program [START_REF] Frisch | Gaussian 09[END_REF] using the B3LYP functional and the 6-31G (d,p) basis set. The optimized structures and the HOMOs and LUMOs are represented in Figure S6. The DPF dyes and dye IV displayed rather distorted structures upon geometry optimization, possibly due to steric hindrance from the donor groups (Table S3) with a large angle (~ 35 ° between the donor and -bridge (A D  Interestingly, the angles between the -bridge and the acceptor (A A  were much smaller than A D . Dyes with a large angle sum (A D plus A A ) form distorted structures projected from the central -bridge. These distorted structures were confirmed by the single crystal X-ray diffraction (XRD) data [31,33,37] of dyes I-III and V, which show larger A D than A A that mached well to the calculated results (Table S3). The interchromophore dye-dye stacking distance increased in the order I < II < V << III (Figure S7). Even though the dyes will probably arrange themselves in a different way in the amorphous thin films, especially in host/guest systems, we can still expect similar steric hindrance and, thus, the same order of proximity between the dyes and their surrounding host molecules.

LEC properties of push-pull dyes without host and with single host

To examine the LEC properties of dyes, we first examined a simple device structure, without a host, where the emitting layer is composed of the dye mixed with tetrahexylammonium tetrafluoroborate (THAB) in a 4:1 molar ratio of dye to electrolyte. This layer was sandwiched between a 35 nm thick poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate (PEDOT:PSS)-coated indium-tin oxide (ITO) anode and a 100 nm thick aluminum cathode (Figure 2, inset). Under a pulsed current (PC) driving mode (50 % duty cycle, 235Hz, Figure S8), red to NIR light emission was observed instantaneously (< 1 s) from the device built with DPF dyes (I-III) (Figure 2a andb). In the following sections, the films and devices with a dye in THAB will be called by the combination of the dye number and initial of THAB, e.g., I/T is the LEC device with dye I and with THAB as electrolyte, without a host.

The EL emission maximum ( em ) of I/T was observed at 630 nm, which is ~10 nm blue-shifted from the PL emission of the I/T film (642 nm). Both the EL and PL of I/T were strongly blue-shifted, by 44 and 32 nm, respectively, compared to the dye's emission in chloroform (674 nm). Moreover, the absorption spectra of I/T films at 483 nm was also blueshifted by 20 nm compared to the that of I in solution (Table 1) or in a host (see below). As described above and by XRD (Figure S7), dye I had the shortest dye-dye distances leading to large dye-dye interactions. The small π-A angle in the crystal structure allows stacking of I on the acceptor side. Thus, the blue-shifts in PL and EL can be attributed to strong dye-dye Htype stacking. The peak EL irradiance of the I/T device was quite low (22 µW/cm 2 ), possibly due to aggregation induced quenching by H-type stacking of dyes and a high injection barrier (Table S4).

On the other hand, II/T showed relatively high EL emission (157 µW/cm 2 ) with a  em at 738 nm (Figure 2a andb), which agrees well with the PL emission maximum of the II/T thin film (737 nm). The EL was red-shifted by 20 nm compared to the PL of II in CHCl 3 (Table S4), indicating that dye II does not form H-type stacking. Dye III showed an impressive EL at  em of 835 nm with a maximum irradiance of 47 µW/cm 2 . The red-shift of the EL and PL emissions of dyes II and III in THAB compared to those in CHCl 3 can be attributed to the strong positive solvatochromism of the push-pull dyes. In contrast, no emission was observed from the IV/T and V/T devices, mainly due to their high injection barrier (Table S4).

In the dye/T devices, carrier transport is limited by the presence of traps, and the excitons generated in the p-n junction experience severe exciton-polaron quenching, reducing the EL emission. [10] Moreover, most push-pull dyes are known to be progressively quenched as the polarity of their medium increases. Indeed, the QY of those push-pull dyes decreased in higly polar solvents (Figure S4), which agreed well with previous reports [30,31] .

To tackle those problems in LEC devices, a host/guest system was employed using the model developed for tris[2-(5-substituent-phenyl)-pyridinato]iridium(III) (Ir(R-ppy) 3 . [10] In this multicomponent host/guest system, excitons are trapped on isolated guest (dye) molecules, hindering exciton diffusion and quenching. Additionally, the use of a host could be beneficial for dye I, as the host molecules reduce the dye stacking described above. Furthermore, nonpolar host molecules would reduce the polarity of the active layer, which should increase the PL efficiency of the push-pull guests.

First, single host systems having only a p-type or n-type host were explored with dye I.

Considering its energy level and p-type nature, poly(9-vinylcarbazole) (PVK) was selected as a p-type host, [10] while two different n-type hosts, BBOT [START_REF] Adachi | [END_REF]40] and OXD-7, were examined to tune the n-doping effect (Figure 2c). The weight ratio of the host (PVK, BBOT or OXD- Moreover, the position of the oxidation and reduction potential of the dyes (Figure 1c) and of the hosts (ref [10] and Figure S9) suggests that doping of the guest is thermodynamically preferable to the host doping. This significantly eliminates traps in the doped region, increasing carrier mobility. Finally, it is important to note that THAB has a large electrochemical stability domain and should not generate side electrochemical reactions.

In the following sections, the devices will be named according to the combination of the initials of the constituents of the active layer (P for PVK, B for BBOT and O for OXD-7), e.g., I/PT represents the LEC device with dye I in a PVK host with THAB as the electrolyte, while I/PBT represents the LEC device of dye I with PVK and BBOT (dual host) with THAB as the electrolyte.

In the I/PT device, EL emission from the guest molecules was observed only at 655 nm (Figure 3a, blue), indicating complete energy transfer from PVK to the dye molecules.

The red-shift of the EL emission (25 nm) and the PL emission (15 nm) of I/PT, compared those of to I/T, indicates less dye aggregation upon addition of the PVK host. However, the maximum intensity was slightly decreased to 14 µW/cm 2 (3.7 Cd/m 2 ), as presented in Figure 3b (top, blue). Compared to the other devices, I/PT operated at a low current density and high voltage, which may be originated from the resistivity of the I/PT layer. Indeed, PVK cannot be electrochemically reduced easily, preventing n-doping and generating unwanted side reactions near the cathode that hinder conductivity. [21] In addition, the electron injection barrier is still high, which could explain the low irradiance.

In the presence of an n-type host only, the EL spectra were blue-shifted, as compared to those of the I/PT devices, shifting to 641 nm for I/BT and 651 nm for I/OT devices (magenta and cyan line, respectively in Figure 3a S4 and Figure S10).

LEC properties of dye with dual hosts

A dual host system with both p-and n-type hosts was explored for dyes with THAB.

The components were blended in a 32.3:32.3:29:6.4 weight ratio for PVK, the electron transporting host, the dye and THAB. The EL emission maximum was 635 nm and 660 nm for I/PBT and I/POT, respectively (red and green line, respectively, in Figure 3a and b top), and the EL irradiance was dramatically enhanced to 425 µW/cm 2 and 386 µW/cm 2 , respectively. The more blue-shifted EL emission of I/PBT compared to I/POT is believed to originate from the lower polarity of BBOT (see later) rather than from aggregation, as there was no blue shift of the absorption of I in the I/PBT film (Table S5, Figure S20). Although it was experimentally dificult to obtain the same thickness of the active layer, due to the difference in the solubility and processability of the hosts (Table S4), the enhancement of EL irradiance was obvious. For I/PBT, the enhancement corresponded to a 19-fold increase compared to that of I/T and a 5-fold increase compared to that of the single host BBOT device (I/BT). Such dramatic enhancement of the dual host LEC system could be ascribed to the balancing of carrier mobility and a decrease in the trap depth. S14b). Both PL and EL showed a red-shift of the emission as the dye concentration was increased from 5 mol % to 23.2 mol %, even though this red-shift was more marked in PL (24 nm) than in EL (9 nm). The morphology of the film with 23.3 mol % was rougher than that of the films with lower dye concentrations, as determined from the SEM images (Figure S15). Thus, further studies on the LEC properties of other dyes and hosts with a dye concentration of approximately 17 mol % were performed.

Dyes II-V were investigated in POT and PBT devices. Figure 3b andc With this dual host, the irradiance of II/PBT was approximately 2.5 times higher than the II/T devices (Table 1) and increased ~10 % compared to II/POT, to reach 397 µW/cm 2 at 705 nm. The irradiance of III/PBT was remarkably enhanced as compared to that of III/T

(2.5-fold) and III/POT (almost 4 times), reaching a peak irradiance of 123 µW/cm 2 at 800 nm.

IV/PBT emitted in the orange region (λ em = 601 nm) with an irradiance of 41 µW/cm 2 , slightly increased compared to that of the IV/POT devices. Finally, V/PBT emitted yellow light (λ em = 550 nm) with a modest peak irradiance of 0.63 µW/cm 2 .

Thus, the dyes showed a large enhancement of the EL intensities upon incorporation in dual host systems. In both POT and PBT devices, only emission from the guest dyes was detected, indicating complete energy transfer from the host to guest molecules. The emission of dyes was higher in the PBT devices than in the POT devices, probably due to the lower E LUMO of BBOT and less exciton quenching from the reduced polarity of the PBT films.

Indeed, when comparing the dye/POT and dye/PBT films, a blue-shift of the PL and the EL was observed upon substitution of OXD-7 by BBOT. This polarity difference can be explained by the different geometry around the central aromatic (2,5-thiophene versus 1,3phenyl) and different partial compensation of the two dipole moments from each acceptor group to the central core of these quadrupoles (calculated ground-state dipole moment of 6.2 D for OXD-7 and 1.3 D for BBOT, as shown in Figure S9b). (Figure S16b), the applied current generates the average probed voltage (V ap ) (Figure 2b for example) that ultimately triggers charge separation and radiation. It is noteworthy that the V ap was sharply increased but doomed to start from the current that turns on irradiance (Figure 2b), which corresponds to V on . This semi-stable V ap region with a sharp increase in radiation corresponds to the radiative recombination region. At a low current density, charge separation occurs as the dyes undergo a redox reaction. At the same time, charge balancing ions move towards each electrode. The cations on the n-doping side (and the anions on the p-doping side) of the p-n junction diffuse towards the emission region, resulting in an ionic concentration gradient over the junction. This opposing voltage generated by the diffusive ion motion decreases the rise in V ap as it compensates for the electrostatic potential over the junction. [41] As the ion drift and transport motion could be affected by the charge separation as well as the p-and n-doping, the voltage profile was largely affected by the dyes and host (Figure 4a andb). As shown in Figure 4a, V on was increased for a larger injection barrier, as expected from the energy levels of dyes, host, and electrodes (Figure 2d). Interstingly, as the CT distance of dyes was increased, V on of the LECs was decreased, mainly due to that the extended conjugation from the donor to acceptor may facilitate carrier transport.

Effect of host on the working voltages and maximum brightness of the devices

A striking feature of the BBOT host is its lower V on and operating voltage compared to those of the OXD-7 devices despite the similar injection barrier, trap depth and CT distance in both host systems (Figure 4a and Figure S10). The voltages at the maximum irradiance (V max )

were also decreased, from the POT devices (9-13 V) to the PBT devices (7-10 V) as summarized in Table S4. This voltage drop for the BBOT host system could originate from the lower bandgap of the PVK/BBOT host as well as the easier electrochemical doping of BBOT compared to OXD-7. Indeed, BBOT is reduced at -1.9 V vs Fc/Fc + (Figure S9a), whereas OXD-7 at approximately -2.2 V vs Fc/Fc + . [10] In addition, considering the different anion and cation volumes (58 Å 3 vs 450 Å 3 ) [42] , the ionic mobility of BF 4 -should be considerably higher than the mobility of THA + . This should lead to higher anion near the anode than cation concentration near the cathode and thus higher p-doping possibilities. Then, the effect of n-doping would be significantly large enough to differentiate the OXD-7 against BBOT.

On the other hand, the peak irradiances of dye/POT and dye/PBT were correlated with several parameters. The irradiance increased as the quantum yield (QY) and lifetime of dyes in solution increased (Figure 4d red and green, respectively). Moreover, it decreased as the electron trap depth or dipole moment transition of the emitting layer increased (Figure S17a andb), except for dye V, due to the enhanced barrier to overcome. Notably, the correlation of the QY of dyes for the solid powder against peak irradiance was poor (Figure S17c), indicating that the dye-dye interaction or H-stacking of dyes are unlikely in the presence of host. Instead, dye-host interactions become important, and the host behaves like a solid solvent. Moreover, the irradiance was inversely proportional to the angle sum (A D plus A A ) (Figure 4b, blue), except for dye V in PBT. As described above, dyes with large angles form distorted structures projected from the central -bridge. These distorted structures would have difficulty interacting with the host because of steric hindrance. Instead, Figure 4b indicates that dyes with more planar structures (low angle sum) could allow proximity between the dyes and their surrounding host molecules (Figure 4c), leading to larger energy transfer from the host to guest dyes and eventually to provide larger irradiance. Dye V in PBT was an outlier from these correlations, possibly due to the low peak irradiance arising from the high injection barrier sum.

As all the devices operate in different current and voltage ranges, it is of interest to compare their peak external quantum efficiency (EQE) and current efficiency . I/POT showed the highest peak EQE (0.49%). Interestingly, the peak EQEs of dye II are quite high considering its emission in the NIR region (Table S4 and Figure S17d).

Operation stability is of great importance for displays. The operation stability of I/POT and I/PBT devices are shown in Figure 4d and e, respectively. It was investigated upon turn-on and turn-off for 3 s with 3 measurements at each cycles at a similar emitted brightness of 20 Cd/m 2 . Both devices showed good switchability over multiple cycles, possibly due to the electrochemical stability of the push pull dyes involved. Notably, the EL intensity of LEC devices increased by 5 ~ 15 % within 5 cycles compared to the first turn-on and then remained stable even after 30 ~ 40 cycles. This increase could be caused by a difference in the doping and dedoping speeds resulting in a growth of the doped region between each cycle, thus emission enhancement. After 300 s, a small but obvious decrease of the EL intensity is observed from the I/PBT device. This could be due to the higher average pulsed current (222 mA/cm 2 ) application to I/PBT as compared to that for I/POT devices (11 mA/cm 2 ), leading to higher joule heating, thus higher temperature and faster degradation. The stability was also investigated upon constant pulsed current application. The average voltage and irradiance variations for I/T (driven at 100 mA/cm 2 ), I/POT (driven at 30 mA/cm 2 ) and I/PBT (driven at 111 mA/cm 2 ) over time are shown in Figure S18. When driven with a constant pulsed current, a decrease of the voltage can be observed during the first 10-20 s and can be attributed to the electrochemical doping of the active layer. The peak irradiance was reached in a few seconds (I/POT) to 10-20 s (I/T and I/PBT). Decent stability was observed for I/T devices with a lifetime to half maximum irradiance (t 1/2 ) of 2650 s. I/POT showed exceptional long-term stability at high irradiance, with an initial 30 % decrease (296 to 202 μW/cm 2 ) during the first 500 s of operation followed by only 20 % diminution over the next 19,500 s, leading to a t 1/2 of more than 20,000 s (t 1/2 > 5.5 h), and an overall progressive 10 % decrease of the voltage. The initial fast decrease of the irradiance could be due to shifting of the position of the p-n junction. [43] Finally, I/PBT devices showed lower stability than the others with a t 1/2 of 90 s. For all devices, it is noteworthy that almost no spectral difference was observed between the emission spectra at maximum irradiance and at half maximum irradiance. The lower stability of I/PBT than the others could be due to over-doping [44] in I/PBT in the stabilized regime, as this device showed the highest conductivity in impedance spectroscopy. Such over-doping could lead to doping-induced exciton quenching and side reactions, to reduce the lifetime of the I/PBT device. To minimize over-doping effect from the constant curr ent experiment, current-off (turn off) state could be introduced between the current-on states as in the current on/off switching cycles above. In Figure 4e, current on/off cycles with 3 s intervals give significantly prolonged stability for the I/PBT device.

Figure 5 presents the maximum irradiance to emission wavelength comparison of NIR (>700 nm) dyes used for LEC devices to date (the structures and references of RX are given in Figure S19). To the best of our knowledge, to date, only 3 LECs with emission maxima above 800 nm have been reported: 2 iridium complex-based LECs and one zinc porphyrinbased LEC (Figure S15). [17,45,46] Hence, LECs incorporating dye III are the most red-shifted metal-free LECs to date. Moreover, the maximum irradiance of the LEC incorporating II and III are in the same range as polymer-based (blue dots), iTMC-based (black dots) or hybrid (iTMC + organic dye or iTMC + polymer, green dots) LECs. These results are particularly remarkable because fluorescent dyes do not harvest triplet excitons, unlike phosphorescent LECs. These results show that well-optimized small dye-based LECs can compete with stateof-the-art polymer or iTMC LECs. Furthermore, this study sheds light on a design principle for yellow to NIR emitting push-pull dyes for LEC devices, taking advantage of their easy tunability of the emission wavelength from the D-π-A structure. In addition, the CT disctance of push-pull dyes could be a convenient tool to reduce working voltage and V on of LECs. The host-guest system was necessary to enhance the irradiance of LECs made of push-pull dyes, which could be further enhanced by 1) optimizing dye-host interactions using a dye with a low distortion angle along the π bridge and acceptor, 2) energy tuning between the host and dyes, and 3) maximizing spectral overlap between the host and dyes.

Conclusion

Push-pull dyes presented high thermal stability, Φ f , simple thin film processiblity, and high tunability in EL wavelength by controlling their structure and host in LECs. In particular, the fluorene-based dyes I-III showed high irradiance comparable to those of state-of-the-art iTMC-based NIR LECs due to their excellent fluorescence properties in solution and in the solid-state. This study sheds light on a design principle for yellow to NIR emitting push-pull dyes for LECs, taking advantage of their easy tunability of the emission wavelength from the D-π-A structure. The dye III showed the most red-shifted metal-free LECs (~ 825 nm) up to date. In addition, the CT disctance of push-pull dyes could be a convenient tool to reduce working voltage and V on of LECs. These results are particularly remarkable because fluorescent dyes do not harvest triplet excitons. Hence, several important design principles were put forward for high LEC emission from push-pull dyes: increased dye-host interaction via low distortion angle between the π-bridge and acceptor, fine energy tuning between host and dyes, and maximization of the spectral overlap between the host and dyes. Finally, we showed that replacing the electron transport material OXD-7 with BBOT led to an increase in the irradiance, arising from better energy transfer from BBOT to the dyes, as well as lower operating bias originating from the lower redox potential of BBOT.

Experimental Section

Materials

Fabrication of LECs:

For the dye/T approach, solutions of the active layer were prepared by dissolving dyes I-V at a concentration of 10 mg/mL with THAB in chlorobenzene with a dye/T molar ratio of 4:1. For the single host approach, dyes were dissolved at a concentration of 10 mg/mL in chlorobenzene, and host materials were added with a weight ratio of host/guest/electrolyte = 64.6:29:6.4. For the multi host/guest approach, the solutions were prepared in a similar way, with a weight ratio of host1/host2/guest/electrolyte =

The thickness of the PEDOT:PSS and the active layer were measured by cross-cut SEM using a field emission scanning electron microscope (JEOL, IT-500HR, imaged after 90 s of Pt coating) and a stylus profilometer (Bruker, DektakXT). S19). The color of the symbols corresponds to the emitter's category: black for metal complex emitter (Ir, Ru, Zn), green for metal complex with organic dye or polymer hybrid, red for organic dye, and blue for organic polymer. When needed, conversion from radiance to irradiance was done assuming Lambertian emission. q) ) 550 (-q) ) 0.63 (-q) ) a)

Maximum absorption wavelength, b) maximum fluorescence wavelength, and c) fluorescence quantum yield of dyes in CHCl 3 solution. d) Lifetime in chlorobenzene solution. e) Maximum fluorescence emission and f) fluorescence quantum yield in solid-state (crystalline powder) measured with calibrated integrated sphere. g) Calculated CT distances. h) HOMO (E HOMO,CV ), LUMO (E LUMO,CV ) energy, and bandgap (E g,CV ) determined from CV (Figure 1c). i) Optical bandgap measured from the onset of the absorption spectra in chloroform. j) From ref [30] , k)

From ref [31] . l) Measured using rubrene as reference (Φ f = 27 % in MeOH). m) From ref [33] .

7 )

 7 Figure 2d, showing the energy levels of dyes are included inside those of the host compounds.

  and b top). More interestingly, the EL intensities of I/BT (78 µW/cm 2 ) and I/OT (151 µW/cm 2 ) were greatly enhanced compared to those of I/PT or I/T devices, corresponding to 6-fold and 12-fold increases, respectively (Figure3a). I/BT and I/OT emitted at similar voltage (Figure3b) but their working voltage was much lower than that of I/PT. This could be attributed to the formation of an effective ndoped region by BBOT and OXD-7, which decreased the resistivity and increased the irradiance of I/BT and I/OT. It is noteworthy that this irradiance increase is correlated to the lowering of the injection barrier or trap depth sum, while the turn-on voltage (V on ) of the devices increased with increasing injection barrier sum or trap depth sum (Table

  Impedance spectroscopy was used to investigate the host's effect on the device's impedance and capacitance (Figures S11-13) with I/T, I/PT, I/POT and I/PBT. First of all, I/PT showed negligible effect of the applied DC voltage (0 to 6 V range) on the resistance and capacitance due to its high turn-on voltage and lack of n-doping capacity. On the other hand, in the I/T, I/POT and I/PBT devices, increased applied voltage results in lower impedance and higher capacitance. Upon electrochemical doping, electrical conductivity was enhanced, notably from traps suppression. The ionic conductivity of the devices was decrease due to the localization of ions near the electrodes by the electrical field. The high resistance values of I/PT and I/POT are well matched with their low current density operation. Finally, the largest ionic conductivity at high voltage was observed for I/PBT devices, which may be attributed to the favorable host-dye I interactions (Fig 4 c) that could provide well-ordered ionic channels for electrical conduction. The influence of the dye concentration on the I/PBT device was investigated. As shown in Figure S14, the maximum emission intensity was increased as the dye concentration was increased up to 17.1 mol % in the mixture. A further increase in the dye concentration (to 23.2 mol %) led to an ~ 5 % decrease in the peak irradiance of the LEC, indicating that the optimum concentration of dye would be approximately 17 ~ 23.2 mol %. In contrast, the PL emission of I/PBT films with the same dye concentrations was inversely proportional to the dye concentration (Figure

  present the voltage and irradiance variations with different applied average current densities and the normalized EL spectra, respectively, for the POT and PBT devices. First, with the POT host, dye IV emitted red light (633 nm) with a peak irradiance of 35 µW/cm 2 , but the V/POT devices were still not emissive. Compared to the devices without a host (II/T), the EL emissions of II/POT and III/POT were blue-shifted by 23 and 10 nm, as expected from the lower polarity. The PL of dye II was also blue-shifted in II/POT. The maximum irradiance was more than doubled for dye II/POT, reaching 364 µW/cm 2 . However, the irradiance of III/POT was halved upon inclusion in this host system. This can probably be explained by the mediocre overlap between the emission of the PVK/OXD-7 host (420 nm) and the absorption of dye III (Figure3c, bottom), resulting in low Förster resonance energy transfer (FRET). On the other hand, the exciplex emission of the PVK/BBOT host (490 nm) perfectly overlaps with the absorption of dyes I and II in chloroform solutions and has a decent overlap with the absorption of III in CHCl 3 (the absorption in the thin films is very close to the absorption in solution; see FigureS16a). Thus, the combination of PVK and BBOT should lead to a more efficient FRET to dye III compared to the PVK/OXD-7 pair. Moreover, the lower LUMO of BBOT (-3.16 eV, Figure2d) should lead to more equilibrated doping states.

Figure 3d presents a

  Figure 3d presents a schematic diagram of the charge separation over a p-n junction,

:

  PVK (poly(9-vinylcarbazole) and THAB (tetrahexylammonium tetrafluoroborate) were used as received from Sigma Aldrich. OXD-7 (1,3-bis[2-(4-tert-butylphenyl)-1,3,4oxadiazo-5-yl]benzene) was purchased from TCI. BBOT (2,5-bis(5-tert-butyl-benzoxazol-2yl)thiophene) was purchased from Alfa Aesar and used as received. Chlorobenzene and other poly(styrene sulfonate) (PEDOT:PSS) and pattered indium-tin-oxide (ITO) glass were purchased from Ossila and Wooyang GMS, respectively.

32. 3 :

 3 32.3:29:6.4. For the dye/PBT LECs, chlorobenzene was replaced with DMAC to maintain film quality. Patterned ITO glass substrates were cleaned by a 20 min ultrasonic bath in 500 ml of DI water and 5 ml of Hellmanex™ III, followed by 20 min in DI water and finally 20 min in acetone. The substrates were then dried in an oven at 80 °C for 1 h to remove any solvent residues before undergoing a UV-ozone treatment for 20 min.PEDOT:PSS (35 nm) was spin-coated on the substrates at 4000 rpm for 60 s before baking at 110 °C for 1 h. The substrates were then transferred into a dry N 2 -filled glovebox. The active layers were then spin-coated at 2000 rpm for 60 s and annealed for 1 h at 70 °C. Finally, a set of four Al (100 nm) electrodes was added on top of the active material by thermal evaporation under high vacuum (5*10 -4 Pa) through a shadow mask.Instruments and characterization:The LECs were driven by a pulsed current mode (235 Hz, 50 % duty cycle) generated by a Keithley 2400 sourcemeter controlled by a MATLAB code.The voltage and radiance of the LEC were measured upon application of a current ramp.Irradiances (from radiances) and EQE were calculated assuming Lambertian emission. TGA was performed on a Q50 analyzer from a TA instrument. DSC was performed on a DSC 200 F from Netzsch. Absorption spectra in solution were measured on a dual beam JASCO 670 spectrometer. Fluorescence spectra in solution and in powder were measured on a Horiba Jobin-Yvon Fluorolog-3® spectrofluorimeter equipped with red-sensitive Hamamatsu R928 or R2658 photomultiplier tubes and reference-corrected for both the excitation source light intensity variation (lamp and grating) and the emission spectral response (detector and grating). The Φ f of IV was measured in dilute solution by a comparative method with rubrene as standard references. Absolute quantum yields in powder were measured as described by de Cyclic voltammetry experiments were conducted at a concentration of 1 mM under an argon atmosphere (glove box) in a standard one-compartment, three-electrode electrochemical cell using a Biologic ESP-300 potentiostat equipped with a 1 A/48 V booster and a linear scan generator. TBAP was used as the supporting electrolyte (0.1 m) in anhydrous dichloromethane. The vitreous carbon (= 3 mm) working electrodes were polished with diamond paste before each recording. A platinum wire counter electrode was used. The Ag + /Ag reference electrode was made of a silver wire immersed in a solution containing a known concentration of Ag + (AgNO 3 (10 -2 M) + tetrabutylammonium perchlorate (0.1M) in acetonitrile). The HOMO and LUMO energy level was calculated from the oxidation (reduction) onset as:

Figure 1 .

 1 Figure 1. (a) Structures of dyes I-V. (b) Molar absorption coefficients (dashed) and normalized fluorescence (solid line) of dyes I (red), II (blue), III (black), IV (orange), and V (green) in CHCl 3 (2.5 μM), excited at their absorption maximum. (c) Cyclic voltammograms of dyes I-V (1 mM) in degassed DCM solutions using tetrabutylammonium perchlorate as the electrolyte (0.1 M), glassy carbon as the working electrode, platinum as the counter electrode, Ag/Ag+ as the reference electrode and a scan rate of 100 mV/s.

Figure 2 .

 2 Figure 2. (a) EL spectra (top) of the LEC device with dyes I (red), II (blue), and III (black) in THAB (4:1 mol. ratio) compared to their photoluminescence spectra (middle) and absorption (bottom) in chloroform solution (2.5 mM). The EL spectra were acquired with a spectroradiometer (below 780 nm) and then with a corrected optical fiber spectrometer. (b) The voltage (empty square) and irradiance (filled) variations under increasing applied average current density for dyes I (blue), II (red), and III (black) in THAB. Inset : Schematic diagram of the LEC structure. (c) The chemical structure of the hosts. (d) Schematic energy level diagram of the dyes and host.

Figure 3 .

 3 Figure 3. (a) EL spectra of the I/T (dotted black), I/PT (blue), I/OT (cyan), I/BT (magenta), I/PBT (red) and I/POT (green) devices at their maximum EL intensity. (b) The voltage (empty square) and irradiance (dots) variations with the applied average current density of the devices of dye I in different medium (top), of dyes/PBT devices (middle) and of the dyes/POT devices (bottom). (c) Normalized EL emission spectra of the dye/PBT devices (top) and of the dye/POT devices (middle) compared to the normalized dye absorption spectra in chloroform and host/T film PL emission spectra (bottom, gray area: POT film and purple area: PBT film) for dye I (red), II (blue), III (black), IV (orange), and V (green). The EL spectra were acquired with a spectroradiometer (before 780 nm) and then with a corrected optical fiber spectrometer. (d) Schematic diagram illustrating the working mechanism of the LECs taking into account the ionic size difference (blue circles: THA + , red circles: BF 4 -, black circles: holes (+) and electrons (-), red lightning bolt: light emission).

Figure 4 .

 4 Figure 4. (a) Correlation between the device turn-on voltage (V on ) and the injection barrier sum (red) and CT distance (blue) for dye I (circle), II (square), III (star), IV (triangle) and V (diamond) in the /PBT (filled shaped) and /POT (open shapes) devices. (b) Correlation of the peak irradiance with the QY yield in solution (red) lifetime in solution (green) and the angle sum (blue, A D + A A ) for dye I (circle), II (square), III (star), IV (triangle) and V (diamond) in the /PBT (filled shaped) and /POT (open shapes) devices. (c) Optimized structures of I and BBOT showing the small A A and possible dye-host interactions on the acceptor side. (d) Stability of the I/POT devices upon 34 cycles of turn-on (~3 s) and turn-off (~3 s) driven by an average pulsed current of 11 mA/cm 2 . (e) Stability of the I/PBT devices upon 41 cycles of turn-on (~3 s) and turn-off (~3 s) driven by an average pulsed current of 222 mA/cm 2 .

Figure 5 .

 5 Figure 5. Comparison of the maximum irradiance of NIR LECs presented herein (square, diamond, pentagon forms) and published previously (half-filled circles; the structures and references of R1-R14 are given in FigureS19). The color of the symbols corresponds to the emitter's category: black for metal complex emitter (Ir, Ru, Zn), green for metal complex with organic dye or polymer hybrid, red for organic dye, and blue for organic polymer. When needed, conversion from radiance to irradiance was done assuming Lambertian emission.

  n)Maximum fluorescence wavelength of dyes in PBT (POT) devices. o) Maximum

Table 1 .

 1 Optical and electrochemical properties of the dyes in solution and crystalline powder

	Dye	λabs a [nm]	λPL b) [nm]	Φf c) [%]	τ d) [ns]	λPL e) [nm]	Φf f) [%]	CT g) [Å]	Dye/PBT (Dye/POT) LECs λPL n) [nm] λEL o) [nm] Irr p) [µW/cm 2 ]
	I	508 j)	674 j)	51 j)	2.5	655	22	4.98	650 (660)	635 (660)	425 (386)
	II	509 k)	718 l)	37 k)	2.1	710	28	5.34	725 (730)	705 (715)	393 (364)
	III	570 k)	788 k)	30 k)	1	800	9	5.37	> 780(> 780)	800 (825)	123 (19)
	IV	425	625	11 l)	0.8	640	45	4.74	615 (620)	601 (633)	41 (35)
	V	440 m)	530 m)	<1 m)	0.2	600	33	3.51	570 (-

Acknowledgements M.R. and J.H. contributed equally to this work. This research was supported by a National Research Foundation (NRF) grant funded by Korean government (Ministry of Science, ICT & Future Planning, MSIP) through the Global Research Lab (GRL: 2016K1A1A2912753), Creative Materials Discovery Program (2018M3D1A1058536), and AOARD, Air Force Office of Scientific Research (AFOSR) under the grant number FA9550-19-S-0003.

Data and materials availability

All data is available in the main text or the supplementary materials.

Received: ((will be filled in by the editorial staff))

Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff))

Mello et al. [47] and Porrès et al. [48] using an integrating sphere collecting all the emission (2π steradians covered with spectralon®, model F-3018 from Horiba Jobin Yvon). The sphere was calibrated with known standards (Coumarin 153, Rhodamine 6G, DMAMS, Erythrosine B and tetraphenylporphyrin). Absorption and fluorescence spectra of thin films were measured on a Perkin-Elmer lambda 750 spectrophotometer and a Konica Minolta CS-2000 spectroradiometer, respectively (with excitation at 365 nm from a UV lamp). The lifetimes of the dyes in chlorobenzene were in the nanosecond range (Figure S3b). The transient decay of dyes I ~ IV can be fitted with a monoexponential, with lifetimes of 2.5, 2.1, 1 and 0.8 ns, respectively. Dye V was fitted with three exponentials. The intensity-weighted average lifetime for V was calculated to be 2.1 ns with the following formula:

(1)

From the three exponentials, the lifetimes of V were determined to be 0.2 (67 %), 3 (21 %) and 10.9 (12 %) ns, which could possibly be attributed to Z/E photoisomerization in solution.

The fastest and major weight (67 %) of 0.2 s is reported in Table 1 and correlated to Figure 4b (green).

The brightness and EL spectra (380-780 nm) of the LEC devices were measured with a Konica Minolta CS-2000 spectroradiometer. Finally, LEC spectra in the 780-940 nm range were obtained with an Avaspec-2048 optical fiber spectrometer corrected in the NIR region by using the fluorescence of 4-dimethylamino-4'-nitrostilbene (4,4'-DMANS). [START_REF] Lakowicz | electroluminescence wavelength of dyes in PBT (POT) devices. p) Peak irradiance of electroluminescence for dyes in PBT (POT) devices. q)[END_REF] Conversion from radiance to irradiance was done assuming Lambertian emission. The impedance spectroscopy analysis was carried out with a universal potentiostat [model CHI 624B (CH Instruments, Inc.)].). The devices were measured in the frequency range from 200 kHz to 1 Hz and with an amplitude of 10 mV. The data were analyzed using the model circuit presented in Figure S11.

Supporting Information

Supporting Information is available from the Wiley Online Library or from the author.

Author Contributions E.K. directed the research project and supervised the experimental design. M.R. and J.H. conducted the experimental research and data analysis. C.B., Y.B., M.R. and C.A. synthesized the fluorescent materials. J. K., S.K. and D.K. contributed to the experimental measurements and characterizations. The manuscript was written by M.R., J.H. and E.K.

Competing financial interests

The authors declare no competing financial interests.

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