Hervé Goëau
email: herve.goeau@cirad.fr

Pierre Bonnet
email: pierre.bonnet@cirad.fr

Alexis Joly
email: alexis.joly@inria.fr

Overview of LifeCLEF Plant Identification task 2019: diving into data deficient tropical countries

Keywords: LifeCLEF, PlantCLEF, plant, expert, tropical flora, Amazon rainforest, Guiana Shield leaves, species identification, fine-grained classification, evaluation, benchmark

Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Introduction

Automated identification of plants and animals has improved considerably in the last few years. In the scope of LifeCLEF 2017 [START_REF] Joly | Lifeclef 2017 lab overview: multimedia species identification challenges[END_REF] in particular, we measured impressive identification performance achieved thanks to recent deep learning models (e.g. up to 90 % classification accuracy over 10K species). Moreover, the previous edition in 2018 showed that automated systems are not so far from the human expertise [START_REF] Joly | Overview of lifeclef 2018: a large-scale evaluation of species identification and recommendation algorithms in the era of ai[END_REF]. However, these 10K species are mostly living in Europe and North America and only represent the tip of the iceberg. The vast majority of the species in the world (about 369K species) actually lives in data deficient countries in terms of collected observations and the performance of state-of-theart machine learning algorithms on these species is unknown and presumably much lower. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") presented in this paper was designed to evaluate automated identification on the flora of such data deficient regions. The challenge was based on a new dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. The average number of images per species in that new dataset is significantly lower than the last dataset used in the previous edition of PlantCLEF [START_REF] Goëau | Overview of expertlifeclef 2018: how far automated identification systems are from the best experts? lifeclef experts vs. machine plant identification task[END_REF] (about 1 vs. 3), and many species contain very few images or may even contain only one image. To make it worse, because of the lack of illustrations of these species in the world, the data collected as a training set suffers from several properties that do not facilitate the task: many images are duplicated across different species leading to identification errors, some images do not represent plants, and many images are drawings or digitalized herbarium sheets that may be visually far from field plant images. The test set, on the other hand, does not present this type of noisy and biased content since it is composed only of expert data identified in the field with certainty. As these data have never been published before, there is also no risk that they belong to the training set. As in the 2018-th edition of PlantCLEF, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out for PlantCLEF 2019. In total, 26 deep-learning systems implemented by 6 different research teams were evaluated with regard to the annotations of 5 experts of the targeted tropical flora. This paper presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Dataset

Training set

We provided a new training data set of 10K species mainly focused on the Guiana shield and the Amazon rainforest, known to be one of the largest collection of living plants and animal species in the world (see figure1). As for the two previous years, this training data was mainly aggregated by bringing together images from complementary types of available sources, including expert data from the international platform Encyclopedia of Life (EoL5 ) and images automatically retrieved from the web using industrial search engines (Bing and Google) that were queried with the binomial Latin name of the targeted species. Details numbers of images and species per sub-dataset are provided in Table 1. A large part of the images collected come from trusted websites, but they also contain a high level of noise. It has been shown in previous editions of LifeCLEF, however, that training deep learning models on such raw big data can be as effective as training models on cleaner but smaller expert data [START_REF] Goëau | Plant identification based on noisy web data: the amazing performance of deep learning (lifeclef 2017)[END_REF], [START_REF] Goëau | Overview of expertlifeclef 2018: how far automated identification systems are from the best experts ?[END_REF]. The main objective of this new study was to evaluate whether this inexpensive methodology is applicable to the case of tropical floras that are much less observed and therefore much less present on the web. One of the consequences of this change in target flora is that the average number of images per species is much lower (about 1 vs. 3). Many species contain only a few images and some of them even contain only 1 image. On the other hand, few common species are still associated with several hundreds of pictures mainly coming from EOL. In addition to this scarcity of data, the use of web search engines to collect the images generates several types of noise:

Duplicate images and taxonomic noise: web search engines often return the same image several times for different species. This typically happens when an image is displayed in a web page that contains a list of several species. For instance, in the Wikipedia web page of a genus, all child species are usually listed but only a few of them are illustrated with a picture. As a consequence, the available pictures are often retrieved for several species of the genus and not only the correct one. We call this phenomenon taxonomic noise. The resulting label errors are problematic but they can paradoxically have a certain usefulness during the training. Indeed, species of the same genus often have a number of common morphological traits, which can be visually similar. For the less illustrated species, it is therefore often more cost-effective to keep images of closely related species rather than not having images at all. Technically, to help managing this taxonomic noise, the duplicate images were replicated in the directories of each species they belong to, but the same image name was used everywhere.

Non-photographic images of the plant (herbarium, drawings): because of data scarcity, it often occurs that the only images available on the web for a given species are not photographs but rather digitized herbarium sheets or drawings from academic books. This typically happens for very rare or poorly observed species. For the most extreme cases, these old testimonies, sometimes more than a century old, are the only existing data. The usefulness of these data for learning is again ambivalent. They can be visually very different from a photograph of the plant but they still contain a rich information about the appearance of the species.

Atypical photographs of the plant: a number of images are related to the target species but do not directly represent it. Typically, it can be a landscape related to the habitat of the species, a photograph of the dissection of plant organs, a handful of seeds on a blank sheet of paper, or a microscopic view.

Non-plant images: search engines sometimes return images that are not plants and that have only a very indirect link with the target species: medicines, animals, mushrooms, botanists, handcrafted-objects, logos, maps, ethnic (food, craftsmanship), etc.

Test set

Unlike the training set, the labels of the images in the test set are of very high quality to ensure the reliability of the evaluation. It is composed of 742 plant observations, all of which have been identified in the field by one of the best experts on the flora in question. Marie-Franoise Prévost [START_REF] Delprete | Marie-françoise prévost fanchon(1941-2013)[END_REF], the author of this test set, is a french botanist, who has spent more than 40 years to study French Guiana flora. She has an extensive field work experience, and she has contributed a lot to improve our current knowledge of this flora, by collecting numerous herbarium specimens of great quality. Ten plant epithets have been dedicated to her, which illustrates the acknowledgement of the taxonomists community to her contribution to the tropical botany. For the re-annotation experiment by the other 5 human experts, only a sub-set of 117 of these observations was used.

Task Description

The goal of the task was to identify the correct species of the 742 plants of the test set. For every plant, the evaluated systems had to return a list of species, ranked without ex-aequo. Each participating group was allowed to submit up to 10 run files built from different methods or systems (a run file is a formatted text file containing the species predictions for all test items).

The goal of the task was exactly the same for the 5 human experts, except that we restricted the number of their responses to 3 species per test item to reduce their effort. The list of possible species was provided to them.

The main evaluation metric for both the systems and the humans was the Top1 accuracy, i.e. the percentage of test items for which the right species is predicted in first position. As complementary metrics, we also measured the Top3 accuracy, Top5 accuracy and the Mean Reciprocal Rank (MRR), defined as the mean of the multiplicative inverse of the rank of the correct answer:

MRR : 1 Q Q q=1 1 rank q
4 Participants and methods 167 participants registered for the PlantCLEF challenge 2019 and downloaded the data set, but only 6 research groups succeeded in submitting run files. Details of the methods are developed in the individual working notes of most of the participants (Holmes [START_REF] Chulif | Plant identification on amazonian and guiana shield flora: Neuon submission to lifeclef 2019 plant[END_REF], CMP [START_REF] Picek | Recognition of the amazonian flora by inception networks with test-time class prior estimation[END_REF], MRIM-LIG [START_REF] Nguyen | Non-local densenet for plant clef 2019 contest[END_REF]). We provide hereafter a synthesis of the runs of the best performing teams: CMP, Dept. of Cybernetics, Czech Technical University in Prague, Czech Republic, 7 runs, [START_REF] Picek | Recognition of the amazonian flora by inception networks with test-time class prior estimation[END_REF]: this team used an ensemble of 5 Convolutional Neural Networks (CNNs) based on 2 state-of-the-art architectures (Inception-ResNet-v2 and Inception-v4). The CNNs were initialized with weights pre-trained on the dataset used during ExpertCLEF2018 [START_REF] Sulc | Plant recognition by inception networks with testtime class prior estimation[END_REF] and then fine-tuned with different hyper-parameters and with the use of data augmentation (random horizontal flip, color distortions and random crops). Further performance improvements were achieved by adjusting the CNN predictions according to the estimated change of the classes distribution between the training set and the test set. The running averages of the learned weights were used as the final model and the test was also processed with data augmentation (3 central crops at various scales and their mirrored version). Regarding the data used for the training, the team decided to remove all images estimated to be non oral data based on the classication of a dedicated VGG net. This had the effect of removing 300 species and about 5,500 pictures. An important point is that additional training images were downloaded from the GBIF platform 6 , in order to fill the missing species and enrich the other species. This increased considerably the training dataset with 238,009 new pictures of good quality totalling then 666,711 pictures. Unfortunately, the participants did not submit a run without the use of this new training data, so that it not possible to measure accurately the impact of this addition. The participant's focus was rather on evaluating different prior distribution of the classes (Uniform, Maximum Likelihood Estimate, Maximum a Posteriori) to modify predictions in order to soften the impact of the highly unbalanced distribution of the training set.

Holmes, Neuon AI, Malaysia, 3 runs, [START_REF] Chulif | Plant identification on amazonian and guiana shield flora: Neuon submission to lifeclef 2019 plant[END_REF]: This team used the same CNN architectures than the CMP team (Inception-v4 and Inception-ResNet-v2). In their case, however, the CNNs were initialized with weights pre-trained on Im-ageNet rather than ExpertCLEF2018 [START_REF] Sulc | Plant recognition by inception networks with testtime class prior estimation[END_REF] and they did not use any additional training data. An original feature of their system was the introduction of a multitask classification layer, allowing to classify the images at the genus and family levels in addition to the species. Complementary, this team spent some efforts to clean the dataset. The 154,627 duplicate pictures were removed and they automatically removed 15,196 additional near-duplicates based on a cosine similarity in the feature space of the last layer of Inception-V4. Finally, they removed 13,341 non plant images automatically detected by using a plant vs. non plant binary classifier (also based on Inception-V4). Overall, the whole training set was decreased by nearly 42% of the images (whereas the CMP team increased it by nearly 53%). Cross-validation experiments conducted by the authors [START_REF] Chulif | Plant identification on amazonian and guiana shield flora: Neuon submission to lifeclef 2019 plant[END_REF] show that removing the duplicates and near-duplicates may allow to gain 4 points of accuracy. In contrast, removing the non plant pictures does not provide any improvement. The introduction of the multi-task classifier at the different taxonomic levels is shown to provide one to two more points of accuracy.

MRIM, LIG, France, 10 runs, [START_REF] Nguyen | Non-local densenet for plant clef 2019 contest[END_REF]: this team based all the runs on DenseNet, another state-of-art CNN architecture which has the advantage to have a relatively low number of parameters compared to other popular CNNs. They increased the initial model with a non-local block with the idea to model interpixels correlations from dierent positions in the feature maps. They used a set of data augmentation processes including random resize, random crop, random flip and random brightness and contrast changes. To compensate the class imbalance, they made use of oversampling and under-sampling strategies. Their cross-validation experiments did show some significant improvements but these benefits were not confirmed on the final test set, probably because of the crossvalidation methodology (based on a subset of only 500 species among the most populated ones).

The three other remaining teams did not provide an extended description of their system. According to the short description they provided, the datvo06 team from Vietnam (1 run) used a similar approach to the MRIM team (DenseNet), the Leowin team from India (2 runs) used Random Forest Boosted on the features extracted from a ResNet, and the MLRG SSN team from India (3 runs), used a ResNet 50 trained for 100 epochs with stratification of batches.

Fig. 2. Scores between Experts and Machine

A very difficult task, even for experts: none of the botanist correctly identified all observations. The top-1 accuracy of the experts is in the range 0.154 -0.675 with a median value of 0.376. It illustrates the difficulty of the task, especially when reminding that the experts were authorized to use any external resource to complete the task, with Flora books in particular. It shows that a large part of the observations in the test may not contain enough information to be identified with high confidence. The complete identification may actually rely on other information such as the root shape, the smell of the plant, type of habitat, the feeling of touch from certain parts, or the presence of organs or feature that were not photographed of visible on the pictures. Only two experts with an exceptional field expertise were able to correctly identify more than 60% of the observations. The other ones correctly identified less than 40%.

Tropical flora is much more difficult to identify. Results are significantly lower than the previous edition of LifeCLEF confirming the assumption that the tropical flora is inherently more difficult to identify than the more generalist flora. The best accuracy obtained by an expert is 0.675 for the tropical flora whereas it was 0.96 for the flora of temperate regions considered in 2018 [START_REF] Goëau | Overview of expertlifeclef 2018: how far automated identification systems are from the best experts? lifeclef experts vs. machine plant identification task[END_REF]. Comparison of medians (0.376 vs 0.8) and minimums (0.154 vs 0.613) over the two years further highlights the gap. This can be explained by the fact that (i) there is in general much more diversity in tropical regions compare to temperate ones, for a same reference surface, (ii) tropical plants in high rainforests, are much less accessible to humans who have much more difficulties to improve their knowledge on these ecosystems, (iii) the volume of available resources (including herbarium specimens, books, web sites) is much less important on that floras.

Deep learning algorithms were defeated by far by the best experts. The best automated system is half as good as the best expert with a gap of 0.365, whereas last year the gap was only 0.12. Moreover, there is a strong disparity in results between participants despite the use of popular and recent CNNs Fig. 3. Scores achieved by all systems evaluated on the two test sets (DensetNet, ResNet, Inception-ResNet-V2, Inception-V4), while during the last four PlantCLEF editions the homogenization of high results forming a "skyline" had often been observed. These differences in accuracy can be explained in part by the way participants managed the training set. Although previous work had shown the effectiveness of training from noisy data [START_REF] Krause | The unreasonable effectiveness of noisy data for fine-grained recognition[END_REF], [START_REF] Goëau | Plant identification based on noisy web data: the amazing performance of deep learning (lifeclef 2017)[END_REF], most teams considered that the training dataset was too noisy and too imbalanced. They made consistent efforts for removing duplicates pictures (Holmes), for removing non plant pictures (Holmes, CMP), for adding new pictures (CMP), or for reducing the classes imbalance with smoothed re-sampling and other data sampling schemes (MRIM). None of them attempted to simply run one of their models on the raw data (as usually done in previous years). So that it is is not possible to conclude on the benefit of such filtering methods this year.

Complementary results

Extending the training set with herbarium data may provide significant improvements. As mentioned in section 4, the CMP team considerably extended the training set by adding more than 238k images of the GBIF platform, the vast majority of these images coming from the digitization of herbarium collections. The performance of their system during the official evaluation was not that good, but unfortunately, this was mainly due to a bug in the formatting of their submissions. The corrected version of their submissions were evaluated after the end of the official challenge and did achieve a top-1 accuracy of 41%, 10 points more than the best model of the evaluation and 3 points more than the third human expert. It is likely that this high performance gain is mostly due to the use of the additional training data. This opens up very interesting perspectives for the massive use of herbarium collections, which are being digitized at a very fast pace worldwide.

Estimation of class purity in the training dataset. In order to evaluate how the different types of noise (herbariums & drawings, other plant pictures and non plant pictures) affect the training set, we computed some statistics based on a semi-automated classification of the training set. More precisely, we repeatedly annotated some images by hand, trained dedicated classifiers and predicted the missing labels. Figure 4 (left side) displays the average proportion of each noise as a function of the number of training images per species. Complementary, on the right side, we display the average proportion of duplicates still as a function of the number of training images per species. If we look at the herbariums & drawings, it can be seen that their proportion significantly decreases up to 150 images per species and then increases again for the most populated species. This evolution has to be correlated with the average proportion of web vs EoL data in the training set. Indeed, the number of web images per species was limited to 150 so that the species with large amounts of training data are mainly illustrated by EoL images. This data is highly trusted in terms of species labels but the figure shows that it contains a high proportion of herbarium sheets. Concerning the two other types of noise, the proportion of other plant pictures is globally increasing likely because this type of picture is also more represented in the EoL data. In contrast, the proportion of non plant pictures is decreasing above 150 images per species which means that this kind of noise is lower in EoL. Concerning the duplicates, it can be seen that their proportion is strongly decreasing with the number of images per species. This means that the absolute number of duplicates is quite stable over all species but that its relative impact is much more important for species having scarce training data. Average accuracy of the best evaluated systems as a function of the number of training images per species To analyze the impact of the different types of noise on the prediction performance, we computed the species-wise performance of a fusion of the best run of each team (focusing on the three teams who obtained the best results CMP, Holmes and MRIM). This was done by first averaging the scores returned by each system and then by computing the mean rank of the correct answer over all the test images of a given species. Figure 5 displays this mean rank for each species (using a color code, see legend) as a function of the number of training images for this species and the estimated proportions of the different noises. The following conclusions can be derive from these graphs:

1. the more images, the better the performance: without surprise, all graphs show that the mean rank of the correct species improves with the number of images. 2. the presence of non plant pictures only affects species with few training data (as shown in the second sub-graph). Well populated species seem to be well recognized even with a very high proportion of non plant pictures. 3. the presence of herbarium data and other plant pictures is not conclusive: as discussed in section 2.1, these two types of contents are ambivalent. They may bring some useful information but they may also disrupt the model. The graphs of 5 do not allow to conclude on this point. 4. a too high proportion of duplicates (above 20%) significantly degrades the results, even for species having between 30 and 200 images.

Conclusion

This paper presented the overview and the results of the LifeCLEF 2019 plant identification challenge following the eight previous editions conducted within CLEF evaluation forum. The results reveal that the identification performance on Amazonian plants is considerably lower than the one obtained on temperate plants of Europe and North America. The performance of convolutional neural networks fall due to the very low number of training images for most species and the higher degree of noise that is occurring in such data. Human experts themselves have much more difficulty identifying the tropical specimens evaluated this year compared to the more common species considered in previous years. This shows that the small amount of data available for these species is correlated with the lowest overall knowledge we have of them. An interesting perspective for the future is to consider herbarium data as one solution to overcome the lack of data. This material, collected by botanists for centuries, is the most comprehensive knowledge that we have to date for a very large number of species on earth. Thus, their massive ongoing digitization represents a great opportunity. Nevertheless, they are very different from field photographs, their use will thus pose challenging domain adaptation problems.

Fig. 1 .

 1 Fig. 1. Regions of origin of the 10k species selected for PlantCLEF 2019: French Guiana, Suriname, Guyana, Brazil (states of Amapa, Para, Amazonas)

Fig. 4 .

 4 Fig. 4. Average proportion of each noise as a function of the number of training images per species (binned with a step size of 10 pictures)

Fig. 5 .

 5 Fig. 5. Mean rank for each species as a function of the number of training images for this species and the estimated proportions of the different noises. The mean ranks greater than or equal to 6 are shown in black.

Table 1 :

 1 Description of the PlantCLEF training sub-datasets.

	Sub-dataset names	# Images # species	Data source
	PlantCLEF 2019 EOL	58,619	4,197	Encyclopedia of Life
	PlantCLEF 2019 Google 68,225	6,277 Automatically retrieved by Google web search engine
	PlantCLEF 2019 Bing 307,407	8,681	Automatically retrieved by Bing web search engine
	Total	434,251 10,000

https://eol.org/

https://www.gbif.org/

Acknowledgements We would like to thank very warmly Julien Engel, Rémi Girault, Jean-François Molino and the two other expert botanists who agreed to participate in the task on plant identification. We also we would like to thank the University of Montpellier and the Floris'Tic project (ANRU) who contributed to the funding of the 2019-th edition of LifeCLEF.

Results

The detailed results of the evaluation are reported in Table 2. Figure 2 gives a more graphical view of the comparison between the human experts and the evaluated systems (on the dedicated test subset). Figure 3, on the other hand, provides a comparison of the evaluated systems on the whole test set.

MRR Whole

Holmes Run 2 0,316 0,247 0,376 0,419 0,357 0,362 0,298 Holmes Run 3 0,282 0,225 0,359 0,376 0,321 0,329 0,274 Holmes Run 1 0,248 0,222 0,325 0,368 0,325 0,302 0,269 CMP Run 7 0,085 0,078 0,145 0,197 0,168 0,124 0,111 CMP Run 2 0,077 0,061 0,145 0,188 0,162 0,117 0,097 CMP Run 6 0,068 0,057 0,154 0,188 0,163 0,112 0,096 CMP Run 1 0,068 0,069 0,145 0,171 0,158 0,107 0,099 CMP Run 3 0,068 0,066 0,128 0,188 0,156 0,110 0,099 CMP Run 4 0,060 0,053 0,128 0,162 0,160 0,097 0,090 MRIM Run 1 0,043 0,042 0,051 0,060 0,088 0,055 0,063 MRIM Run 8 0,034 0,046 0,068 0,103 0,102 0,057 0,068 MRIM Run 7 0,026 0,042 0,085 0,094 0,096 0,053 0,065 datvo06 Run 1 0,026 0,043 0,051 0,060 0,086 0,041 0,061 CMP Run 5 0,026 0,054 0,085 0,085 0,119 0,050 0,078 MRIM Run 10 0,026 0,034 0,068 0,068 0,085 0,047 0,057 MRIM Run 5 0,017 0,036 0,043 0,077 0,082 0,039 0,058 MRIM Run 3 0,017 0,030 0,060 0,077 0,088 0,043 0,054 MRIM Run 2 0,017 0,036 0,043 0,077 0,082 0,039 0,058 MRIM Run 6 0,017 0,028 0,051 0,077 0,078 0,037 0,049 MRIM Run 9 0,017 0,031 0,043 0,068 0,088 0,039 0,055 MRIM Run 4 0,009 0,027 0,060 0,077 0,077 0,038 0,049 MLRG SSN Run 1 0,000 0,000 0,000 0,000 0,000 0,000 0,000

Leowin Run 1 0,000 0,000 0,000 0,000 0,001 0,000 0,000 MLRG SSN Run 2 0,000 0,000 0,000 0,000 0,000 0,000 0,000 MLRG SSN Run 3 0,000 0,012 0,000 0,009 0,027 0,004 0,021 Leowin Run 2 0,000 0,000 0,000 0,000 0,001 0,000 0,000 The main outcomes we can derive from that results are the following ones:


Alexandre Govin
email: govin@emse.fr

Marie-Claude Bartholin

Wolfram Schmidt

Philippe Grosseau

Combination of superplasticizers with hydroxypropyl guar, effect on cement-paste properties

Keywords: Admixture, Rheology, Adsorption, Polymers, Pore solution

Viscosity-modifying agent (VMA) are used in several applications of concrete, such as underwater concrete, Self-Compacting Concrete (SCC) or Self-Levelling Underlayments (SLU) in order to improve the washout resistance and the stability. The study focuses on the modifications of cement pastes properties implied when a VMA (hydroxypropyl guars or HPG) and a superplasticizer are introduced together. For reaching this objective, two chemically different polycarboxylate-based superplasticizers (PCE) and two HPGs exhibiting different molar substitution ratios (MSHP), were studied. A method, combining total organic carbon and size exclusion chromatography, was developed in order to quantify the adsorption of the both admixtures. The adsorption of HPGs appears being significantly lowered by the presence of PCE, while only the adsorption of the less charged PCE is slightly affected by the HPG. As consequence, strong modifications of the rheological properties of cement pastes were noticed when HPG and PCE are combined. The desorption of HPG leads to higher yield stress and residual viscosity than with PCE alone.

Introduction

For many years, cementitious materials exhibiting high fluidity have been developed motivated by their economic and technical advantages. In order to achieve this high fluidity, special admixtures were designed and introduced in the formulations of cementitious materials. Among the admixture classes, superplasticizers, also known as high-range-water-reducing (HRWR) admixtures, are commonly used for this purpose. These admixtures allow to dramatically reduce the amount of mixing water, while maintaining, or even enhancing the workability and the performances [START_REF] Hanehara | Interaction between cement and chemical admixture from the point of cement hydration, absorption behaviour of admixture, and paste rheology[END_REF][START_REF] Yamada | Effects of the chemical structure on the properties of polycarboxylate-type superplasticizer[END_REF][START_REF] Nkinamubanzi | Superplasticizers in practice[END_REF]. Today's typically used superplasticizers (SPs), based on polycarboxylate ethers (PCE), exhibit a comb-like structure. They consist of a polyanionic backbone partially esterified with non-ionic side chains of polyethylene oxide (PEO). The ionic charges, conferred by the deprotonated carboxylic groups in the backbone, allow the PCE to adsorb on the positively charged surfaces of cement particles and hydrated phases. The non-adsorbing side chains are responsible for the dispersing effect through steric repulsion forces between particles [START_REF] Yamada | Effects of the chemical structure on the properties of polycarboxylate-type superplasticizer[END_REF][START_REF] Plank | Impact of zeta potential of early cement hydration phases on superplasticizer adsorption[END_REF][5]. According to Flatt et al., the adsorption of these admixtures depends mainly on the amount of free carboxylic groups, while the steric repulsions are influenced by the number and the length of the side chain [5]. However, inhomogeneities or poor stability, such as segregation or bleeding, may occur for highly flowable cementitious materials, which can compromise durability of concrete. To overcome this adverse effect, viscosity-modifying admixtures (VMA) are often introduced into the formulation [START_REF] Khayat | Effect of Welan Gum-High-Range Water Reducer Combinations on Rheology of Cement Grout[END_REF][START_REF] Rols | Effects of different viscosity agents on the properties of self-leveling concrete[END_REF][START_REF] Lachemi | Selfconsolidating concrete incorporating new viscosity modifying admixtures[END_REF][START_REF] Sonebi | Rheological properties of grouts with viscosity modifying agents as diutan gum and welan gum incorporating pulverised fly ash[END_REF]. VMA are widely used to improve stability, cohesion and robustness of many concrete applications, such as self-compacting concrete, self-leveling underlayment concrete, under water concrete, shotcrete… VMA may be classified as organic or inorganic-based materials as proposed by Kawai [START_REF] Kawai | Non Dispersible Underwater Concrete Using Polymers[END_REF]. Inorganic VMAs consist in high specific surface area powders such as colloidal silica, fine silica fume, fly ash or calcium carbonate which increase the stability of highly flowable concrete, or swelling materials such as bentonite for improving water retention. The organic VMAs are hydrophilic and water soluble polymers often classified into three classes, i.e. natural, semi-synthetic and synthetic polymers. Synthetic VMAs include mainly polyethylene oxide (PEO) or polyacrylamides, while natural and semi-synthetic VMAs consist mainly in natural polysaccharides (starch, welan gum, diutan gum, guar gum, xanthan gum, alginate, agar…) and their derivatives (cellulose ethers, modified starch, gar gum derivatives…). Among them, sphingan gums (welan and diutan gum), starch, cellulose ethers (hydropropyl methyl cellulose, hydroxylethyl cellulose, carboxymethyl cellulose), are the most frequently used. These admixtures cause modifications of the rheological behavior of the cementitious materials by increasing both the apparent viscosity and the yield stress [START_REF] Khayat | Viscosity-enhancing admixtures for cement-based materials -An overview[END_REF][START_REF] Paiva | Effects of a water-retaining agent on the rheological behaviour of a single-coat render mortar[END_REF][START_REF] Patural | Cellulose ethers influence on water retention and consistency in cement-based mortars[END_REF]. The admixed cementitious materials generally exhibit a shear-thinning behavior. The viscosity mainly influences the properties of concrete during the flow process (casting and application), such as the filling velocity, the resistance to flow and the segregation stability during the flow. The yield stress is more linked to the properties of flowable concrete after casting by improving the stability at rest (no segregation) and the maintaining of the shape at rest. The present paper is focusing on the influence of Hydroxypropyl Guar (HPG) as VMA in presence of PCE. HPGs are semi-synthetic polymers already widely used in many industrial fields, such as textile printing, hydraulic fracturing process, oil production or paper manufacturing [START_REF] Risica | Rheological properties of guar and its methyl, hydroxypropyl and hydroxypropyl-methyl derivatives in semidilute and concentrated aqueous solutions[END_REF][START_REF] Lapasin | Flow properties of hydroxypropyl guar gum and its long-chain hydrophobic derivatives[END_REF]. These admixtures provide high water retention capacity in cementitious materials, making these admixtures suitable for the construction industry. Several studies were devoted to the estimation of the water retention capacity induced by the HPGs and to their mechanism of action [START_REF] Plank | Applications of biopolymers and other biotechnological products in building materials[END_REF][START_REF] Poinot | Impact of hydroxypropylguars on the early age hydration of Portland cement[END_REF][START_REF] Poinot | Importance of coil-overlapping for the effectiveness of hydroxypropylguars as water retention agent in cement-based mortars[END_REF][START_REF] Cappellari | Influence of organic thickening admixtures on the rheological properties of mortars: Relationship with water-retention[END_REF][START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF]. The chemical structure of the HPGs, such as the molecular mass, the amount of hydropropyl groups grafted on the backbone, characterized by the MS (MSHP), and the nature of the grafting, appear as crucial parameters for the water retention properties [START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF][START_REF] Poinot | Use of ultrasonic degradation to study the molecular weight influence of polymeric admixtures for mortars[END_REF]. Indeed, these structural parameters affect the dosage necessary to reach the coil overlapping concentration, corresponding to the concentration from which, the isolated polymer coils, existing at low polymer concentration, come into contact. This overlapping leads to the establishment of polymer aggregates in the aqueous phase, which is responsible to the improvement of the water retention of cementitious materials [START_REF] Poinot | Importance of coil-overlapping for the effectiveness of hydroxypropylguars as water retention agent in cement-based mortars[END_REF][START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF]. The HPGs also have capacity to enhance the stability of cementitious materials through the increase in either the yield stress and/or the viscosity [START_REF] Cappellari | Influence of organic thickening admixtures on the rheological properties of mortars: Relationship with water-retention[END_REF][START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF]. HPGs have the advantage of being less expensive than the microbial polysaccharides such as welan and diutan gum, and less impacting the environment than cellulose derivatives. Indeed, thanks to its chemical structure, guar gum is extracted by simple thermo-mechanical process and the chemical modification of the guar gum requires normal reaction conditions of temperature and pressure, generates less by-products and requires minimal purification procedure [START_REF] Biasotti | Highly substituted hydroxypropylguar: a strong contribution to construction chemistry[END_REF].

Since formulations developed by concrete industry become more and more complex and contain several admixtures, a better understanding of the effects induced by admixtures is essential. A sound interpretation of the mechanism is required for tailoring concrete performances for special practical applications. It was previously shown that the combination of admixtures may lead to competitive adsorption which can cause unintentional effects [START_REF] Plank | Competitive adsorption between superplasticizer and retarder molecules on mineral binder surface[END_REF] but also enhance the effectiveness of the admixtures [START_REF] Lummer | Combination of lignosulfonate and AMPS®-co-NNDMA water retention agent-An example for dual synergistic interaction between admixtures in cement[END_REF][START_REF] Bessaies-Bey | Organic admixtures and cement particles: Competitive adsorption and its macroscopic rheological consequences[END_REF]. Üzer and Plank studied the impact of welan gum on the dispersing ability of PCE [START_REF] Üzer | Impact of welan gum stabilizer on the dispersing performance of polycarboxylate superplasticizers[END_REF]. The authors showed that the welan gum strongly reduced the fluidity induced by the PCE. According to them, the loss of fluidity is caused by the increase in the liquid phase viscosity, and no competitive adsorption between PCE and welan gum occurred. On the contrary, it is reported that competitive adsorption could occur when PCE is combined with cellulose ether [START_REF] Bessaies-Bey | Organic admixtures and cement particles: Competitive adsorption and its macroscopic rheological consequences[END_REF]. Indeed, depending on PCE dosage, cellulose ether could be partially or totally desorbed by the presence of PCE, leading to modification of the rheological behavior of cement pastes (decrease in the yield stress and increase in the viscosity).

The objective of this paper is to provide improvement in the understanding of synergetic or competitive effects between polycarboxylate-based superplasticizers and hydroxypropyl guars, by focusing on adsorption and rheology. In order to quantify the specific adsorption onto cement particles of both admixtures (HPG and PCE) upon combined addition to the mix, a suitable method is proposed. The method consists in a combination of total organic carbon (TOC) measurements and size exclusion chromatography (SEC) experiments. The consequence of the adsorption on the rheological properties of the cement paste is highlighted through the yield stress and the viscosity. The effect of the chemical composition and structure of HPGs is studied. For this purpose, two HPGs with an increasing amount of hydroxypropyl groups grafted were selected. Also two different PCEs were chosen in order to highlight the influence of the charge density of PCE.

Materials and methods

Mineral products

Ordinary Portland cement (CEM I 42.5 R CEMEX according to the European standard EN 197-1) was used for all the study [START_REF]EN 197-1, Cement -Part 1: Composition, specifications and conformity criteria for common cements[END_REF]. The chemical composition and the phase composition of the cement were determined by X-ray fluorescence spectroscopy and XRD analysis (D5000, Siemens) combined with Rietveld refinement method (Siroquant V2.5). The results are given in Table 1.

Organic admixtures

In this paper, two different commercial polycarboxylate-based superplasticizers (PCE), provided by BASF, were studied and labeled PCELC and PCEHC respectively. Further details are given in Table 2. The main difference between the two superplasticizers lies in their charge density, which may strongly affect their performance [START_REF] Plank | Competitive adsorption between superplasticizer and retarder molecules on mineral binder surface[END_REF]. Indeed, a high charge density should promote the adsorption of the admixture onto the cement surface [START_REF] Winnefeld | Effects of the molecular architecture of comb-shaped superplasticizers on their performance in cementitious systems[END_REF][START_REF] Plank | Experimental determination of the effective anionic charge density of polycarboxylate superplasticizers in cement pore solution[END_REF]. The carboxylate groups present in each PCE was determined by conductimetric titration using a 0.1 M NaOH solution. The anionic charge density supplied by carboxylate sites of superplasticizers (SPs)

was expressed in mmol of charge per g of polymer. Between the two superplasticizers, PCELC and PCEHC exhibit the lowest and the highest charge density, respectively (Table 2). This is consistent with the decreasing grafting degree from PCELC to PCEHC and characterized by a decrease in the b/a ratio (Fig. 1(a)). Briefly, the guar gum is a hydrophilic and non-ionic natural polysaccharide extracted from the endosperm of Cyamopsis tetragonolobus seed. Guar gum is composed of D-mannopyranose backbone (β(1-4) bond) with random branchpoints of galactose via an α(1-6) linkage. The manufacture of HPGs is more environmentally friendly than for cellulose derivatives since its extraction requires a simple thermo-mechanical process and its branched-chain structure, with a lot of hydroxyl groups, provides a high chemical reactivity and solubility in cold water [START_REF] Biasotti | Highly substituted hydroxypropylguar: a strong contribution to construction chemistry[END_REF]. 2 and Fig. 1(b). The both polysaccharides exhibit similar molecular weight, around 2.10 6 g.mol -1 [START_REF] Poinot | Use of ultrasonic degradation to study the molecular weight influence of polymeric admixtures for mortars[END_REF]. The number of hydroxypropyl groups per anhydroglucose unit, characterized by the molar substitution (MSHP) was less than 3 for the two HPGs (value provided by the manufacturers). However, HPG 1 and HPG 3 differ by their molar substitution ratio (MSHP), which increases from HPG 1 to HPG 3 (qualitative description).

Preparation of cement pastes

The effect of admixtures was investigated on water-cement system, i.e. cement pastes, with a constant deionized water to solid ratio W/C=0.4. Prior to add water, the hydroxypropyl guar was hand-premixed with cement during one minute. The superplasticizers were added into the dry mix at the same time as the water. In order to maintain a constant water-to-cement ratio, the water contained into the PCE solution was taken into account and deduced from the total mixing water.

All admixtures were studied without pre-dissolution in order to be the closest to the application.

The mixing was made in a blender according to EN 196-1 standard [START_REF]Methods of testing cement -Part 1: Determination of strength[END_REF]. All the dosages in admixture were expressed in percent by weight of cement (% bwoc). The dosage in HPGs was fixed to 0.1% bwoc while the dosage in PCEs was ranged from 0.1% bwoc to 0.3% bwoc.

All tests were carried out in triplicate and at a controlled temperature (20 ± 2 °C). Cement without admixture was also studied and was considered as reference.

Rheological behavior

The rheological properties of cement pastes were investigated thanks to a Rheometer Viskomat NT (Schleibinger Geräte) equipped with a double gap cell and a basket probe made of a structured grid (Vogel cell). The room was thermostated at 20 °C.

At the end of the mixing procedure, the paste was introduced into the measurement cell and held at rest for several minutes. 9 min after contact between water and cement, the rheological protocol was started. The cement pastes were sheared at 100 s -1 for 1 min in order to homogenize the sample and to remove its shear history (potential thixotropic character) [START_REF] Phan | Influence of organic admixtures on the rheological behaviour of cement pastes[END_REF][START_REF] Roussel | The origins of thixotropy of fresh cement pastes[END_REF]. The rheological tests were performed and recorded at 15 min (after 5 min of rest). The experimental protocol consisted in applying 14 decreasing shear rate steps, ranging from 100 s - 1 to 0.06 s -1 , on the cement pastes. Since steady state is necessary to obtain valuable data, the duration of each step was adjusted.

The cement paste rheological properties was correctly described by Herschel-Bulkley (HB) model [START_REF] Herschel | Measurements of consistency as applied to rubber-benzene solutions[END_REF]:

0 n K      (1)
where 0, K and n are the yield stress, the consistency coefficient and the fluidity index, respectively.

The fluidity index represents the shear-thinning or shear-thickening characteristic of a suspension.

The viscosity was examined through the residual viscosity. The residual viscosity was determined according to the following equation, as previously defined by Hot et al. [START_REF] Hot | Adsorbing polymers and viscosity of cement pastes[END_REF].

0 - (γ) app residual viscosity     (2)
Where µapp corresponds to the apparent viscosity (µapp=

()   
) and 0 is the yield stress previously determined by Herschel-Bulkley model.

The advantage of the residual viscosity lies in the fact that the effect of the yield stress on the apparent viscosity is subtracted [START_REF] Hot | Adsorbing polymers and viscosity of cement pastes[END_REF]. The contribution of the energy required to break the inter-particle network, responsible of the yield stress, and to overcome the van der Waals attractive force are deducted. Consequently, the residual viscosity becomes more representative of the energy dissipation when the suspension flows.

Viscosity of the aqueous phase

The rheological properties of the supernatant was investigated by means of a rheometer (MCR 302, Anton-Paar) equipped with cone and plate geometry (2° cone angle, 50 mm diameter), thermostated at 20°C. The supernatant was extracted as described in the next section (section 2.6. dedicated to the adsorption measurements). The steady shear flow of aqueous phases was investigated using decreasing logarithmic ramps in the 10 3 -1 s -1 range.

Flow curves ( () f  

), exhibiting typical shear thinning behavior with a Newtonian region in the low shear rate range, were correlated with the Cross model [START_REF] Cross | Rheology of non-Newtonian fluids: A new flow equation for pseudoplastic systems[END_REF]:

0 n η -η η(γ)=η + 1+(αγ)   (3)
where η0 is the zero-shear rate viscosity, η∞ the infinite rate viscosity, α a relaxation time and n a non-dimensional exponent.

The aqueous phase viscosity was characterized by the zero-shear rate viscosity which represents the viscosity at the Newtonian plateau occurring at low shear rate.

Adsorption measurements

In order to obtain the adsorption isotherms, the non-adsorbed polymer remaining within the aqueous phase was quantified. The adsorption was calculated using the depletion method. The aqueous phase of fresh cement paste (around 150 g) was first extracted, 15 min after the mixing start, by means of a centrifugation step (5000 rpm for 5 min). A second centrifugation step (14500 rpm for 10 min) was applied to the previously extracted supernatant for avoiding the presence of particles in the solution. Despite that a small amount of polymer could be intercalated into the hydrates due to early chemical interactions [START_REF] Flatt | A simplified view on chemical effects perturbing the action of superplasticizers[END_REF], the amount of missing polymer in the aqueous phase was considered as "adsorbed" for the rest of the paper.

Total Organic Carbon (TOC) measurements:

Prior to analysis, the extracted supernatant was diluted 10 times with 0.1 mol.L -1 hydrochloric acid solution. The total organic carbon content in the extracted solutions was determined by combustion under pure oxygen at 850°C (Vario-TOC Cube, Elementar). Reference solutions of HPG (250mg/L) and PCE (650mg/L) were used to calibrate the device for each experimental series. The initial organic carbon contained in pure cement, such as grinding aids, was taken into account and deduced after its quantification in cement aqueous phase without admixture.

Size Exclusion Chromatography (SEC) measurements:

In the present paper, two kinds of admixtures containing carbon (HPG and PCE) were introduced in the cement pastes. TOC analysis is a suitable method for quantifying the adsorption of each admixture when they are introduced separately. However, when the two admixtures are introduced together in the cement paste, TOC method is not able to differentiate the origin of the carbon content. A specific method was therefore developed to determine the concentration of the two admixtures in the aqueous phase. In order to quantify solely the amount of PCE in the aqueous phase, size exclusion chromatography (SEC) was selected. This chromatographic method separates molecules relating to their size and hydrodynamic radii. Fig. 2(a) shows that thanks to the high difference in the molecular weight of the both kind of admixtures (HPGs and PCEs) studied here, the respective peaks are sufficiently separated for a suitable integration of the peak area ascribed to PCE (from 16.4 to 21 min). As all chromatographic analysis, the peak area is proportional to the polymer concentration. The calibration curves obtained by SEC for the both PCEs are given Fig. 2(b).

These highlight a great proportionality between the integrated signal given by the refractive index detector and the PCE concentration. After calibrating of the both analysis methods (SEC and TOC), the PCE concentration was first quantified by SEC and subtracted to the TOC concentration. This allows to calculate the HPG concentration remaining in the aqueous phase. NaCl solution, on-line degassed, was used as eluent. The flow rate was fixed at 0.5 mL.min -1 .

The separation module consisted in Shodex column (OHpak SB-806M-HQ) heated at 50 °C. A refractometer detector (Waters 2410) was used to achieve the detection.

Results and discussions

Adsorption curves and discussion

The methodology was first validated by comparing the concentration of each PCE (introduced alone) given by SEC and by TOC analyses, which is the commonly technique used for quantifying organic polymer adsorption onto cement particles. The results in Fig. 3 confirm that the concentrations of PCEs obtained by SEC and by TOC are identical. This validates the use of SEC as a suitable method for quantifying the amount of PCE remaining in the aqueous phase and thus its adsorption. Where Qads (mg of polymer /g of cement) is the amount of polymer adsorbed, Qplateau the maximum amount of polymer adsorbed, K (L/g) the Langmuir sorption equilibrium constant corresponding to the ratio of adsorption and desorption rates ka and kd (the rates are equal at the equilibrium), and c (g/L) the equilibrium concentration in solution.

Fig. 4. Adsorption isotherm for PCELC ( ), PCEHC ( ), HPG1 ( ) and HPG3 ( ).

The maximum adsorbed amount (Qplateau) and the Langmuir constant (K) are summarized in Table 3 for the four polymers

The Fig. 4 and Table 3 highlight the higher adsorption of PCEHC than PCELC. Indeed, PCEHC exhibits higher affinity and plateau value than PCELC, which is coherent with the difference in the charge density, the grafting degree and the molecule size of the two PCEs. Indeed, lower side chain density and higher charge density promote the adsorption of PCEs on cement surface [START_REF] Plank | Competitive adsorption between superplasticizer and retarder molecules on mineral binder surface[END_REF][START_REF] Winnefeld | Effects of the molecular architecture of comb-shaped superplasticizers on their performance in cementitious systems[END_REF][START_REF] Marchon | Molecular design of comb-shaped polycarboxylate dispersants for environmentally friendly concrete[END_REF][START_REF] Del | Effect of polycarboxylate-ether admixtures on calcium aluminate cement pastes. Part 1: Compatibility studies[END_REF][START_REF] Dalas | Modification of the rate of formation and surface area of ettringite by polycarboxylate ether superplasticizers during early C3A-CaSO4 hydration[END_REF]. For HPGs, it is important to point out that no real plateau was reached and the given value was determined by extrapolation for comparison (Fig. 4, Table 3). It has been previously showed that HPGs can also adsorb on mineral surface, and therefore on cement particles and the depleted amount of HPG determined by the depletion method was not the result of physical entrapment of polymer molecules into the inter-particles space during the centrifugation step [START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF][START_REF] Wang | Adsorption mechanism of guar gum at solidliquid interfaces[END_REF]. However, it can be mentioned here that the nature of the interaction, i.e. electrostatic interaction or hydrogen bonding with the polar cement phases, is not fully known.

The increase in hydroxypropyl substitutions grafted on the guar (increasing MSHP from HPG1 to HPG3) results in a drop in the adsorption plateau without significantly changing the Langmuir constant. These results are consistent with previous studies conducted on HPGs or cellulose ethers (CE) in cementitious materials [START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF][START_REF] Brumaud | Cellulose ethers and yield stress of cement pastes[END_REF] and may be attributed to conformational differences induced by the decrease in the polarity of the polysaccharide, and to the increase in the hydrodynamic radius of the molecules.

HPGs exhibit greatly lower Langmuir constant than PCEs. This result highlights the better affinity of PCEs with the surface of cementitious particles than HPGs. The difference in affinity between the two admixtures may result from their strongly different chemical nature and their behavior in alkaline media. Indeed, many parameters may influence the adsorption and/or competitive adsorption of polymers, such as their chemical composition, molecular structure, molecular mass, the pH, the ionic strength… [START_REF] Ramachandran | Competitive adsorption of polyelectrolytes: A size exclusion chromatographic study[END_REF][START_REF] Hoogeveen | Polyelectrolyte Adsorption on Oxides I. Kinetics and Adsorbed Amounts[END_REF][START_REF] Geffroy | Molar mass selectivity in the adsorption of polyacrylates on calcite, Colloids Surfaces A Physicochem[END_REF]. The difference in affinity was checked by zeta potential measurements carried out on admixture solutions. In order to be close to real cementitious solution in term of composition, ionic strength, pH…, the admixture (HPG or PCE) was dissolved into a cement pore solution (without cement particles). The pore solution was previously extracted by centrifuging a non-admixed cement paste (W/C=0.4) as described in section 2.6. Then, admixture was dissolved in the pore solution in order to reach a concentration of 1 g.L -1 . The zeta potential of HPG-based fluid was around -15mV. The negative zeta potential indicated that the guar gum molecules contained chemical groups that could produce negative charges. As proposed by Wang et al. [START_REF] Wang | Effect of pH on the rheological properties of borate crosslinked hydroxypropyl guar gum hydrogel and hydroxypropyl guar gum[END_REF], this negative zeta potential indicates that, in alkaline media, the guar gum molecules interact with OH -anions to form hydrogen bonds, leading to negatively charged side chains. This is consistent with the large amount of polar groups on HPGs, such as hydroxyl groups, leading to interactions between the admixtures and the polar cement phases [START_REF] Wang | Adsorption mechanism of guar gum at solidliquid interfaces[END_REF][START_REF] Lombois-Burger | On the frictional contribution to the viscosity of cement and silica pastes in the presence of adsorbing and non adsorbing polymers[END_REF]. However, a strongly lower zeta potential (around -60mV) was obtained when the PCE was dissolved in the cement pore solution (without cement particles). This is coherent with the fact that polymethacrylic acid should be fully deprotonated at pH higher than 10 [START_REF] Pohlmeier | Ionization of short polymethacrylic acid: titration, DLS, and model calculations[END_REF]. As consequence, in aqueous phase of fresh cement paste, which is highly alkaline (pH ≈ 12.5), the PCEs can strongly chelate with Ca 2+ ions as counter-ions on the mineral surface [START_REF] Plank | Experimental determination of the effective anionic charge density of polycarboxylate superplasticizers in cement pore solution[END_REF] promoting its adsorption on the surface of cement particles through electrostatic interactions [START_REF] Yamada | Controlling of the adsorption and dispersing force of polycarboxylate-type superplasticizer by sulfate ion concentration in aqueous phase[END_REF].

The adsorption isotherms for the two PCEs (PCELC and PCEHC), in presence of 0.1% of HPG (HPG1 or HPG3), are depicted in Fig. 5, Fig. 6 and Fig. 7. In addition, the adsorption values of each admixture (PCELC, PCEHC, 0.1% of HPG1 and 0.1% of HPG3) introduced alone in the cement paste was represented for comparison. The most important point is that PCE and HPG adsorb onto the surface of cement particles, even when they are introduced together in the pastes (Fig. 5, Fig. 6 and Fig. 7). However, the absence of adsorption regimes where both admixtures adsorb as they are alone in the cement paste is noticed. This regime could exist but at lower dosages than those tested in this paper because it requires low surface coverage to avoid competitive adsorption [START_REF] Fleer | Polymers at interfaces[END_REF]. By increasing the dosage in admixtures, the surface coverage becomes sufficiently high to expect competitive adsorption between admixtures [START_REF] Bessaies-Bey | Organic admixtures and cement particles: Competitive adsorption and its macroscopic rheological consequences[END_REF][START_REF] Fleer | Polymers at interfaces[END_REF]. It is why all the experiments in this study, were carried out with high admixture dosages (> 0.1% bwoc for the PCE and 0.1% bwoc for HPG) in order to enforce a high surface coverage of the cement particles and then to promote competitive adsorption.

The combination of PCELC with HPG1 leads to reduction in the adsorption of the two polymers in comparison with when they are introduced alone (Fig. 5). The results are depicted in Fig. 8 and expressed in mg of polymer adsorbed per g of cement, and in percentage in relation to the adsorption of the admixture introduced alone in the mix. The reduction in adsorption ranges The effect of the charge density of PCE is highlighted in Fig. 6. It appears obvious that the charge density of the superplasticizer affects the behavior of the admixtures. Indeed, when the charge density of PCE increases (PCEHC), its adsorption is unmodified by the presence of HPG1. On the contrary, a strong reduction in the adsorption of HPG1 is pointed out in presence of PCEHC. The reduction in adsorption ranges from 37% to 56% with increasing dosage in PCE (from 0.1% to 0.3%). These values are approximately three times higher than those obtained with the low charge density PCE (Fig. 8). The combination of PCEHC with HPG3 (higher MSHP than HPG 1) leads also to a negligible modification of the adsorption of PCEHC but to a huge reduction of HPG3 adsorption (Fig. 7).

For PCE concentrations of 0.1 and 0.2 %, the adsorption of HPG3 is reduced of about 0.36 mg/g and 0.46 mg/g, respectively. These values correspond to a strong drop in adsorption of about 67 % and 87%, respectively (Fig. 8). In comparison, for the same concentrations in PCEHC, the reduction in adsorption was only of 37% and 39% (0.27 mg/g and 0.29 mg/g) for HPG1, respectively. Despite a lower adsorption of HPG3 without PCE, than HPG1, HPG3

appears to be more sensitive to the presence of the PCE. This result may indicate a decrease in the bonding energy, involved in the adsorption mechanism between the HPG and the surface of cement phases, induced by the MSHP increases. The presence of 0.3% of PCEHC leads to a reduction in adsorption of approximately 0.45 mg/g for the both HPGs (HPG3 and HPG1), indicating an insufficient surface available for the polysaccharide, whatever the HPG. From the results, it appears clearly that the affinity of the polymer with the cement particles is a key factor in the process of competitive adsorption. Indeed, the polymer with the highest affinity and/or diffusion mobility adsorbs first, leaving only the remaining surface or adsorption sites, available for the second admixture. Here, the PCEs exhibit higher affinity than the HPGs (Table 3), causing that the PCE adsorbs first followed by the HPG.

When the dosage in PCEHC increases the steric hindrance and the reduction of free adsorption sites lead to a decreasing surface available for the polysaccharide. As consequence, the adsorption of this latter is reduced. It is worth noting that even at 0.3% bwoc of PCEHC, the HPG is able to adsorb since its adsorption is not null. This result suggests that either the dosage in PCE is not enough to completely inhibit the adsorption of the HPG, or a low adsorption of HPG is still possible through a potential change in conformation of the adsorbed molecule, or interactions between the HPG and the adsorbed PCE occur, or a low amount of HPG is entrapped during the hydration process. Furthermore, the cement hydrates continue to grow

HPG3 + PCE HC

PCE LC

providing additional free surfaces for adsorption. More investigations are needed to conclude on this point.

It can also not be excluded that the adsorption of PCE is favored in comparison to the guar gum because of the higher mobility of PCE in the aqueous phase. Indeed, the kinetic aspects of the adsorption were neglected here. The initial adsorption rate may contribute to the preferential adsorption of the PCE compared to the HPG. Indeed, Hoogeveen et al. showed that initial adsorption rate is usually controlled by the rate of transport, and then by the diffusion coefficient, of the non-adsorbed polymer from the bulk solution to the surface which is promoted by the small size of molecules [START_REF] Hoogeveen | Polyelectrolyte Adsorption on Oxides I. Kinetics and Adsorbed Amounts[END_REF]. As consequence, the adsorption of PCE may be kinetically stimulated, since PCE exhibited a lower size than HPG (Table 2). The dissolution kinetic of admixtures was also neglected. In the present study, the HPG was not pre-dissolved in the mixing water, whereas the PCEs were provided in aqueous solution (20 or 30% of PCE solid in water depending on the PCE). Obviously, the HPG needs first to dissolve prior to adsorb onto the surface of cement particles. The dissolution step could, therefore, limit the amount of HPG adsorbed at 15 min despite that at least 85% of HPG is dissolved after the 5 min of the mixing protocol [START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF].

Competitive adsorption between the PCE and the polysaccharide may occur if the PCE charge density decreases. In this case, the gap of affinity between each admixture and the surface of cement particles is reduced. Therefore, the two admixtures tend to adsorb together, leading to steric hindrance and a drop in the surface available for the both admixtures.

Rheological properties of fresh cement pastes

This section is dedicated to the impact of the competitive adsorption on the rheology of the cement pastes. The evolution as a function of the PCE dosage of the two rheological parameters studied in this paper, i.e. the yield stress obtained from Herschel-Bulkley model and the residual viscosity, are represented in Fig. 9 and Fig. 10. As expected, the yield stress, as well as the residual viscosity, decreases by adding PCE into the cement pastes (Fig. 9). Moreover, higher is the PCE dosage; lower are the two rheological parameters. The steric repulsive forces induced by the adsorbed PCE lead to a reduction of the Van der Waals attractive forces, which causes the reduction of the yield stress. The increase in adsorbed PCE with its dosage contributes to the rise of the inter-particle distance, which may be responsible to the drop in the residual viscosity. The yield stress, as well as the residual viscosity, is also affected by the charge density of PCE. Indeed, for a given dosage, the two rheological characteristics are lower for the pastes admixed with PCEHC than with PCELC. This is consistent with the greater adsorption of PCEHC (Fig. 4). Indeed, steric repulsions and limitation of contacts between cement particles are improved by high adsorption. It is interesting to note that higher dosage in PCE is required in order to reach similar rheological behavior when the charge density of PCE decreases. This is in accordance with a previous study [START_REF] Schmidt | Effects of the Characteristics of High Range Water Reducing Agents and the Water to Powder Ratio on Rheological and Setting Behavior of Self-Consolidating Concrete[END_REF]. In our study, the addition of 0.1% bwoc of PCEHC leads to same values of yield stress and residual viscosity than 0.2% bwoc of PCELC. For 0.2% bwoc of PCEHC, 0.3% bwoc of PCELC is required. when the HPGs (0.1% of HPG1 and 0.1% of HPG3) are combined with PCEHC.

PCEHC alone ( ), 0.1% HPG1 alone ( ), 0.1% HPG3 alone ( ), 0.1% HPG1 + PCEHC ( ) and 0.1% HPG3 + PCEHC ( ) Fig. 9 and Fig. 10 also show the increase in both yield stress and residual viscosity when 0.1% bwoc of HPG is introduced alone into the cement paste. This finding is consistent with the observation that polysaccharides, such as HPGs or cellulose ethers, can adsorb on cement particles, increasing the yield stress through bridging flocculation [START_REF] Govin | Modification of water retention and rheological properties of fresh state cement-based mortars by guar gum derivatives[END_REF][START_REF] Brumaud | Cellulose ethers and yield stress of cement pastes[END_REF]. However, a strongly higher yield stress is obtained with the HPG exhibiting the highest MSHP (HPG3), despite an important reduction in its adsorption, and therefore in its possible bridging capacity (Fig. 10).

The decrease in adsorption of the HPG3 leads to the rise in the concentration of the nonadsorbed polysaccharide molecules remaining in the aqueous phase. This rise in the nonadsorbed HPG coils could compensate the loss of bridging by increasing either the viscosity of the aqueous phase and/or attractive depletion flocculation forces [START_REF] Palacios | Compatibility between Polycarboxylate and Viscosity-Modifying Admixtures in Cement Pastes[END_REF]. The increase in viscosity of the interstitial fluid is highlighted in Fig. 11(b), where the zero-shear viscosity of the aqueous phase of the cement paste admixed with HPG3 is twice higher than that obtained with HPG1.

The increase in the residual viscosity is also a consequence of the partial adsorption of HPG.

Indeed, as mentioned previously, the viscosity of the aqueous phase, and therefore the viscous dissipation, is promoted by the non-adsorbed HPG coils [START_REF] Lombois-Burger | On the frictional contribution to the viscosity of cement and silica pastes in the presence of adsorbing and non adsorbing polymers[END_REF][START_REF] Poinot | Influence of hydroxypropylguars on rheological behavior of cement-based mortars[END_REF][START_REF] Brumaud | Influence Of High Molar Mass Polymers On The Rheological Behavior Of Fresh Cement Pastes[END_REF]. However, it can be noted that polysaccharides could induce specific additional repulsive forces which lead to a lower increase in cement pastes viscosity than expected by the rise of the aqueous phase viscosity [START_REF] Lombois-Burger | On the frictional contribution to the viscosity of cement and silica pastes in the presence of adsorbing and non adsorbing polymers[END_REF][START_REF] Poinot | Influence of hydroxypropylguars on rheological behavior of cement-based mortars[END_REF][START_REF] Brumaud | Influence Of High Molar Mass Polymers On The Rheological Behavior Of Fresh Cement Pastes[END_REF]. with PCEHC (0.1% HPG1 alone ( ), 0.1% HPG3 alone ( ), PCEHC alone ( ), 0.1% HPG1 + PCEHC ( ) and 0.1% HPG3 + PCEHC ( ))

When an increasing dosage in PCE is combined with 0.1% of HPG, a general decrease in both yield stress and residual viscosity is observed (Fig. 9 and Fig. 10). However, whatever the PCE dosage, the combination of the two admixtures leads always to a higher yield stress than PCE alone. This could be the result of the attractive depletion force induced by the non-adsorbed polysaccharide. Depletion forces are the results of osmotic effect induced by differences in concentration of non-absorbing polymer in a suspension. In order to reach equilibrium, the polymer molecules move to the lower concentration zone, leading to attractive or repulsive depletion forces between particles. A recent paper was dedicated to the study of the effect of depletion forces induced by the non-adsorbing polymer on the yield stress [START_REF] Bessaies-Bey | Non-adsorbing polymers and yield stress of cement paste: Effect of depletion forces[END_REF]. The authors showed that in a system containing a superplasticizer, as well as, polyethylene oxide glycol (PEG), the non-adsorbing polymer coils (PEG) could be at the origin of attractive depletion forces between particles. In this case, the attractive depletion forces participate to the flocculation mechanism, characterized, at macroscopic scale, by an increase in the yield stress.

The charge density of the PCE also affects the evolution of the rheological parameters when PCE is combined with HPG. The yield stress continuously decreases with increasing dosage in PCEHC, while a plateau followed by a decrease is observed with PCELC. These evolutions are coherent with the gradual reduction in HPG adsorption induced by PCEHC (Fig. 5 and Fig. 6).

Consequently, the bridging created by the adsorbed HPG is gradually replaced by the steric repulsive forces induced by the adsorbed PCE. In the case of PCELC, it may be assumed that the drop in HPG adsorption could be sufficiently low to preserve bridging and the low desorption of PCELC limits the steric repulsive forces. These two effects allow to maintain a comparable yield stress than with HPG1 alone. When the dosage in PCELC is higher than 0.1% bwoc, the steric repulsion forces implied by the PCE leads to the decrease in the yield stress.

The residual viscosity of the cement paste is also affected by the combination of HPG1 and PCELC. However, the residual viscosity is slightly different than that obtained with PCELC alone, while the aqueous phase exhibits similar viscosity than with HPG1 alone (Fig. 11(a)).

These results indicate that the rise of the hydrodynamic forces induced by the non-adsorbed polysaccharide is not sufficient to overcome the modifications of the colloidal and electrostatic forces induced by the PCE. When the charge density of the PCE rises, it was previously shown that the concentration of non-adsorbed HPG increases (Fig. 8), as well as the viscosity of the aqueous phase (Fig. 11(a)). This can justify the greater residual viscosity of the pastes obtained with the combination of PCE and HPG than with the PCE alone.

The impact of the MSHP on the rheological behavior of cement paste admixed with PCEHC and HPG is illustrated in Fig. 10. In of HPG3, a strong reduction in the yield stress, as well as the residual viscosity, can be observed for increasing dosage in PCEHC. Despite HPG3 provides a greater initial yield stress than HPG1, the yield stress decreases faster when PCEHC is added. This means that the increase in the amount of non-adsorbed HPG3 in the aqueous phase, resulting from its huge desorption and which may promote attractive depletion forces, is not sufficient to compensate the loss of bridging, and the steric repulsion induced by the PCE.

However, the high amount of HPG coils in the aqueous phase contributes to the great increase in the aqueous phase viscosity (Fig. 11(b)), and therefore to the residual viscosity of cements pastes compared to cement paste admixed with PCE alone.

Conclusions

This paper deals with the effect of the combination of PCEs and HPGs on their adsorption and on the rheological behavior of admixed cement pastes. A suitable method composed of TOC and SEC analyses is proposed. This method allows the quantification of the relative adsorption of the two different admixtures present in a cement paste. This method could be used, in further researches, to study the effect of the combination of superplastisizers and VMA in flowable industrial applications, such as Self-Compacting Concrete (SCC) or Self-Levelling Underlayments (SLU). The results show that the adsorption of the two polymers may be affected when HPG is combined PCE superplasticizer. More specially, the adsorption of the polysaccharide decreases. However, if the charge density of the PCE decreases, this admixture becomes sensitive to the presence of the polysaccharide and its desorption may occur.

The consequences of the adsorption/desorption of admixtures on rheological properties of cement pastes are studied. It was shown that the rheological properties are strongly modified by the addition of the two kinds of admixtures. Despite the introduction of the HPG, the relative viscosity of the cement pastes decreased when the dosage in PCE increases. However, the residual viscosity is higher than with PCE alone. This may be the consequence of the increase in concentration of the non-adsorbed HPG coils in the aqueous phase resulting from the reduction in adsorption. The mixture of PCE with HPG allows preserving a significant yield stress to the cement paste in comparison to PCE alone. The charge density of the PCE seems to affect the rheological properties by amplifying the drop of the viscosity and by reducing the improvement of yield stress induced by HPG. The MSHP mainly affects the yield stress of the cement paste and the viscosity of the pore solution but leads to lower effect on the viscosity of the cement paste. According to the results, HPG3 (high MS) may be more suitable for SCC application than HPG1 since the yield stress could be sufficiently low to allow the flow while the viscosity of the paste is greater in order to improve the stability.

bFig. 1 .

 1 Fig. 1. Molecular structure of polycarboxylate ether (PCE) superplasticizer (methacrylic acid

H

  Two hydroxypropyl guars (HPGs) were studied and provided by Lamberti S.p.A. They are qualitatively described in Table

Fig. 2 .

 2 Fig. 2. Illustration of SEC analysis for different concentrations in PCEHC (0.5 to 4 g/L) and for

Fig. 3 .

 3 Fig. 3. Validation of the quantification method by correlating the PCE adsorption determined

Fig. 5 .

 5 Fig. 5. Adsorption isotherms of HPG1 alone ( ), PCELC alone ( ), HPG1 ( ) and PCELC (

Fig. 6 .

 6 Fig. 6. Adsorption isotherms of HPG1 alone ( ), PCEHC alone ( ), HPG1 ( ) and PCEHC (

Fig. 7 .

 7 Fig. 7. Adsorption isotherms of HPG3 alone ( ), PCEHC alone ( ), HPG3 ( ) and PCEHC

Fig. 8 .

 8 Fig. 8. Reduction in the adsorption of: PCELC and HPG1 when they are introduced together,

Fig. 9 .

 9 Fig. 9. Effect of the PCE charge density (PCELC and PCEHC) on the yield stress (a) and the

Fig. 10 .

 10 Fig. 10. Effect of the MSHP of the HPG on the yield stress (a) and the residual viscosity (b)

Fig. 11 .

 11 Fig. 11. Evolution of the aqueous phase zero-shear viscosity as a function of the PCE dosage:

Table 1

 1 Oxide composition and clinker phase composition of cement (%, weight).

				Chemical composition (% wt)
	SiO2	Al2O3	Fe2O3	CaO	MgO	Na2O	K2O	SO3	free CaO	LOI
	20.56	4.36	2.27	62.80	2.14	0.28	0.95	3.45	1.2	2.60
				Phase composition (% wt)
	C3S	C2S		C3A	C4AF	CŠ	CŠ,0.5H	CČ
	68.1	12.8		8.7	6.6	1.8	0.8	1.2

Table 2

 2 Qualitative description of the PCEs and HPGs used.

		PCELC	PCEHC	HPG1	HPG3
	Backbone	Methacrylic acid, identical for both	-	-
	Side chains	Polyethylene oxide (PEO)	-	-
	Side chain length	Medium + low	Medium	-	-
	Grafting degree	High	Low	-	-
	Backbone charge density a	Low	High
	Backbone charge density b (mmol/g)	0.86	1.19	-	-
	Solid content (%)	30	20	-	-
	MSHP	-	-	Low	High
	Hydrodynamic radius (nm) *	13	10	26	35
	a Given by the provider

Table 3

 3 Langmuir parameters for adsorption of PCEs and HPGs after fitting of experimental data.

		PCEHC	PCELC	HPG1	HPG3
	Maximum adsorbed amount Qplateau (mg/g)	0.83	0.55	7.01	2.86
	Langmuir equilibrium constant K (L/g)	4.01	3.09	0.17	0.16

Zero shear viscosity (Pa.s) Solid PCE content (%bwoc) 0.1% HPG1 alone PCE HC alone PCE LC alone 0.1% HPG1 + PCE HC 0.1% HPG1 + PCE LC a)

	0.1							0.1
	0.01							0.01
	0.001							0.001
	0.00%	0.05%	0.10%	0.15%	0.20%	0.25%	0.30%	0.00%	0.05%	0.10%	0.15%	0.20%	0.25%	0.30%

Zero shear viscosity (Pa.s) Solid PCE content (%bwoc) PCE HC alone 0.1% HPG1 alone 0.1% HPG3 alone 0.1% HPG1 + PCE HC 0.1% HPG3 + PCE HC b)


We Choose Who to Cite, Who to Recognize --Please Do So Carefully

A couple of years ago I ran across an article revealing the remarkably evil side of a still currently celebrated biologist active from the 1877 to 1920. The article "Symbiogenesis: the Hidden Face of Constantin Merezhkowsky" [START_REF] Sapp | Symbiogenesis: the hidden face of Constanin Merezhkowsky[END_REF], by its title, is clearly more than an analysis of the contribution of Merezhkowsky to the theory of endosymbiosis (the origin of eukaryotic cell). It importantly exposed a very sordid life as a serial rapist of young girls, an outspoken proponent of racist proto--Nazi views (e.g., proposing the castration of all Jewish males), and death by an elaborate suicide. Nearly 100 years after his death, Merezhkowsky is regularly cited, sometimes unavoidably but many times not. Here I hope to show the obvious: one should actually have read the papers one cites and also consider if and how a paper should be cited. Backstory Previous to "symbiogenesis" Merezhkowsky worked as taxonomist, describing many diatoms and other protists both, planktonic and benthic. The revelations concerning Merezhkowsky struck a nerve as I had included a portrait of Merezhkowsky along with other pioneers and stars of plankton studies in my website of taxonomic monographs (Dolan 2018). Recently I re--read the paper and I believe the facts merit re--telling 17 years after the Sapp et al. paper appeared. The brief historical account below relies entirely on [START_REF] Sapp | Symbiogenesis: the hidden face of Constanin Merezhkowsky[END_REF].

The Traveling Rapist and Diatomologist

In 1888 Merezhkowsky suddenly left the Crimea because of charges of pedophilia, abandoning his wife and young son to a life of poverty. He traveled to the Russian Biological Station in Villefranche--sur--Mer where he stayed for a few months. He then moved on to California; first to work in Northern California, apparently avoiding cities with large Russian immigrant communities. There, after being accused of raping a 14--year--old girl, he fled to Southern California. He worked at the Biological Station of San Pedro collecting and describing diatoms (Merschkowsky 1901), from many sites throughout California. In 1902 he returned to Russia to a post at Kazan University. It was at about this time that he turned his attention to the origin of the chloroplast. Inn 1914, shortly before the outbreak of WW1, Merezhkowsky again fled Russia, not because of his anti--Semitic views and activities, nor his spying for the Czar, but because of a pedophilic sex scandal. He was charged with raping 26 young girls between 1905 and 1914, one as young as 7 years old. It was a nation--wide scandal and in the newspapers he was labeled the 'Marquis de Sade of Kazan'. He never returned to Russia. Merezhkowsky spent the years of WW1 in France moving between Paris, Menton, and Nice. After WW1 he moved to Geneva and there in 1921 he committed suicide in an elaborate manner tying himself to his bed and gassing himself with a custom gas mixture and gas mask.

The Legacy of a Criminal

What is the legacy today of Constantin Merezhkowsky? First one could consider surprising the number, eight, and variety of species named for him: diatoms, invertebrates and ciliates, over a period of nearly 100 years, as shown in Table 1. The last species named for him was in 1968 by Alain Sournia who 11 years later would serve as an Editorial Board Member for JPR from the inaugural issue and onward for many years. In my opinion Sournia was not aware of the sordid side of Merezhkowsky, nor were the others who named species for him, as his reputation outside Russia was likely largely unknown before the appearance of [START_REF] Sapp | Symbiogenesis: the hidden face of Constanin Merezhkowsky[END_REF]. Nonetheless, the list does serve as a sharp reminder that caution should be exercised in honoring a person by naming a species. [START_REF] Sournia | Diatomées planctoniques du Canal de Mozambique et de l'ile Maurice[END_REF]) ___________________________________________________________ *planktonic taxa A bibliometric approach to his current legacy is to consider relatively recent citations of Merezhkowsky, specifically, citations made well after the appearance of [START_REF] Sapp | Symbiogenesis: the hidden face of Constanin Merezhkowsky[END_REF], as shown in Figure 1. The citations from 2009 to 2018 are shown parsed into 2 groups, those with cites concerning specific organisms (mostly papers on diatoms), and those with cites concerning symbiogenesis. The citations concerning diatoms and other organisms can be considered as likely unavoidable as citation of original taxonomic authorities is often obligatory. Another matter entirely are the citations of Merezhkowsky with regard to Endosymbiotic Theory, especially those papers which honor him as the source of the theory by citing "Symbiogenesis: the Hidden Face of Constantin Merezhkowsky" without any mention of his dark side (Fig 1 ). The citations to [START_REF] Sapp | Symbiogenesis: the hidden face of Constanin Merezhkowsky[END_REF] with regard to Merezhkowsky and endosymbiotic theory are especially noteworthy because the paper not only points out the evil side of Merezhkowsky, but it also clearly states that he was not the originator of the proposition that chloroplasts are derived from endosymbiotic algae. As the paper states, it was suggested by many people, for example specifically by Schimper as early as 1883 [START_REF] Schimper | Über die Entwicklung der Chlorophylkörner und Farbkörner[END_REF], 22 years before Merezhkowsky. Furthermore, Merezhkowsky did little or no actual field or experimental work on the topic. Unlike Merezhkowsky, Famitizan (1889) worked extensively on algal symbiosis, on a large variety of organisms (including planktonic tintinnid ciliates) and he attempted to culture chloroplasts outside the algal cell, all well before Merezhkowsky's oft--cited 1905 paper. In fact, the sole originality of Merezhkowsky, with regard to the origin of the chloroplast, was proposing cyanobacteria as a symbionts based simply on the relatively simple structural characteristics of cyanobacteria. It seems rather unjust then that, since 2009, Merezhkowsky 1905 has been cited 123 times compared to Schimper 1883 cited but 37 times and Famintzin not cited at all. In place of a portrait of the criminal Merezhkowsky, Figure 2 shows two of the relatively neglected pioneers of the endosymbiotic theory of the origin of the chloroplast: Andreas Franz Wilhelm Schimper and Andrei Sergeyevich Famintsyn. One might speculate that in writing a book one pays more attention to how one cites than when writing an article? Take Home Lesson As scientists we are the producers of publications through writing, the arbitrators of publications through reviewing, and the users of publications through our citing of previous work. In writing we use other publications. We have considerable freedom to cite or not cite. In reviewing we have freedom to

Fig. 1 .

 1 Fig. 1. The number of publications by year which cited or containing the word "Merezhkowsky", or one of the spelling variants of the name. Publications were parsed into 2 groups: taxonomic publications in which his name appears as a taxonomic authority (largely dominated by diatom studies), and a second group in which he is mentioned or cited in reference to "Endosymbiotic Theory". Numbers at the top of the column indicate the number of papers which cited the Sapp et al. 2002 paper in attributing an endosymbiotic origin of chloroplasts to Merezhkowsky without any mention of his dark side nor the work of his predecessors. Citation data from Google Scholar retrieved July 5, 2019.

Fig 2 .

 2 Fig 2. Neglected pioneers of the theory of the endosymbiotic origin of chloroplasts: Andrea Franz Wilhelm Schimper and Andrei Sergeyevich Famintsyn.

Table One Species named for Merezhkowsky ___________________________________________________________ 1882 Diatom

 One

	Achnanthosigma mereschowskii (Reinhardt, 1882)
	1885 Acorn Worm
	Saccoglossus mereschkowskii (Wagner 1885)
	1889 Diatom
	Triceratium mereskovskii (Pantocsek, 1889)
	1910 Diatoms
	Navicula mereschkowskii (Müller 1910)
	Sellaphora mereschkowskii (Müller 1910)
	1914 Hydroid
	Thuiaria mereschkowskii (Kudelin 1914)
	1929 Tintinnid Ciliate
	*Metacylis mereschkowskii (Kofoid & Campbell
	1929)
	1932 Hypotrich Ciliate
	*Stichotricha merschkowski (Kahl 1932)
	1968 Diatom
	Licmosphenia mereschkowskyi

advise authors on what to cite or not cite. Here I hope to have convinced that it is important to have actually read the papers one cites and then to carefully consider exactly how a paper should be cited. Relatively few among us I hope will unthinkingly cite or distinguish a proto--Nazi and serial child rapist whose work was not unquestionably original.

ACKNOWLEGEMENTS

Dave Montagnes, Sabine Agatha, Maya Dolan, and Lulu Stadler kindly provided comments on a previous version of the text. However, I retain full responsibility for all errors of fact and interpretation. REFERENCES Anton, T. (2017) Planet of Microbes: the Perils and Potential of Earth's Essential Life Forms., University of Chicago Press, Chicago. Archibald, J. (2017) One plus One Equals One: Symbiosis and the Evolution of Complex Life. Oxford University Press, Oxford. Dolan JR 2018. http://aquaparadox.obs--vlfr.fr/html/ClassicMonographs.php Famintzin, A. (1889) Beitrag zur Symbiose von Algen und Thieren. Mémoires de l'Académie Impériale des Sciences de St. Pétersbourg, 7 sér., 36, n° 16. Kahl, A. (1932) Urtiere oder Protozoa I: Wimpertiere Kofoid, C.A., and Campbell, A.S. (1929). A conspectus of the marine and freshwater ciliata belonging to the suborder Tintinnoinea, with descriptions of new species principally from the Agassiz Expedition to the Eastern Tropical Pacific 1904--1905. Univ. Calif. Pub. Zool., 34, 403 Kudelin, N.V. (1914) Hydraires (Hydroidea). Volume II. Livraison 2. Zu Faune de la Russie et des pays limitrophes fondee principalement sur les collections du chusee Zoologique de l'Academie Imperiale des Sciences de Petrograd, Petrograd Mereschkowsky, C. 1901. A list of California diatoms. Ann. Mag. Nat. Hist. 7, 292--300, 474--480, 505--520. Mereschkowsky, C. (1905). Uber natur und ursprung der chromatophoren im pflanzenreiche. Biologisches Centralblatt, 25, 293--604. Müller, O. (1910). Bacillariaceen aus dem Nyassaland und einigen benachbarten Gebieten. IV Folge, Naviculoideae--Naviculeae--Naviulinae, Fragilarioideae--Fragilarieae--Fragilarineae, Fragilarioideae--Fragilarieae--Eunotiinae. (Engler's) Botanische Jahrbucher fur Systematik, Pflanzengeschichte, und Pflanzengeographie. Leipzig, 45(1): 69--122