Tilahun Yeshambel
email: tilahun.yeshambel@uog.edu.et

Josiane Mothe
email: josiane.mothe@irit.fr

Yaregal Assabie
email: yaregal.assabie@aau.edu.

Amharic Document Representation for Adhoc Retrieval

Keywords: Adhoc Retrieval, Amharic, Complex Morphology, Stem, Root

Amharic is the official language of the government of Ethiopia currently having an estimated population of over 110 million. Like other Semitic languages, Amharic is characterized by complex morphology where thousands of words are generated from a single root form through inflection and derivation. This has made the development of tools for Amharic natural language processing a non-trivial task. Amharic adhoc retrieval faces difficulties due to the complex morphological structure of the language. In this paper, the impact of morphological features on the representation of Amharic documents and queries for adhoc retrieval is investigated. We analyze the effects of stem-based and root-based approaches on Amharic adhoc retrieval effectiveness. Various experiments are conducted on TREC-like Amharic information retrieval test collection using standard evaluation framework and measures. The findings show that a rootbased approach outperforms the conventional stem-based approachthat prevails in many other languages.

INTRODUCTION

Searching digital information on the Web or document collection has long become part of the human daily life. Information Retrieval (IR) is the task of searching relevant documents to a user query from document collection. Both the research community and the industry have been very active in this field for more than 60 years [START_REF] Sanderson | The history of information retrieval research[END_REF]. Nowadays, IR has gained much attention due to the explosion of digital data and the need of accessing relevant information from huge corpus quickly and accurately.

IR systems work based on documents representing natural languages, and consequently, the characteristics of a given language affects the whole process of IR [START_REF] Moukdad | A comparison of root and stemming techniques for the retrieval of Arabic documents[END_REF]. Thus, natural language processing (NLP) has attracted the attention of IR communitysince a long time [START_REF] Smeaton | Progress in the application of natural language processing to information retrieval tasks[END_REF][START_REF] Jackson | Natural language processing for online applications: Text retrieval, extraction and categorization[END_REF][START_REF] Cambria | Jumping NLP curves: A review of natural language processing research[END_REF]. For example, NLP applications and resources provide a means to find better representative terms for indexing and query terms that improve search results. This calls for the need of dealing with language specific issues to improve the performance of IR systems. The morphology, orthography, tokenization, syntax, semantics, and compound splitting of a language are some of the issues to be considered while developing IR systems. It has long been understood that linguistic variation has significant impact on IR effectiveness as it leads to the omission of relevant documents to users' queries [START_REF] Moukdad | A comparison of root and stemming techniques for the retrieval of Arabic documents[END_REF]. Many languages have different forms generated from a single word due to morphology and orthography. Identifying the basic units of words is more difficult for morphologically complex languages than for simple languages. Performing simple matching between words generated from the same root is not applicable to capture similarity.

Thus, in order to come up with an effective IR system, one has to deal with the complex characteristics of the language. One of the key features of Amharic is its complex morphology which itself leads to complex grammatical structure. As a result, finding better representations for documents and queries has been an issue of theoretical discussion in Amharic IR. The forms that can be considered for document and query representation are stems and roots.In many languages, the use of surface forms of words to represent documents and queries is not taken into consideration due to the proliferation of words that can be generated from a single root form. This issue is imperative in the development Amharic IR. Although some efforts have been made to develop Amharic IR systems using stems, their effectiveness with respect to the use of various forms has not been systematically analyzed thus far. Therefore, thisresearch analyzes the use of stems and roots for content representation and investigates their effects on Amharic IR.

The rest of this paper is organized as follows. Section 2 describes Amharic language and its morphology. Section 3 discusses related work and Section 4 presents how documents and queries are represented in Amharic IR system. Experimental results and evaluation are discussed in Section 5. In Section 6, we make conclusion along with the way forward in Amharic IR.

AMHARIC LANGUAGE

Amharic is the official language of the government of Ethiopia. Although several languages are spoken in Ethiopia, Amharic is spoken as a mother tongue by a sizeable proportion of the country's population currently estimated to be over 110 million. Among the Semitic language family, it is the second most spoken language in the world, next to Arabic. Due to its historical significance and official status, Amharic has been serving as the lingua franca of the country since a long time. As a result, many literary works, government documents, educational materials, religious literary works, etc. are predominantly produced in Amharic. Amharic uses Ethiopic script for writing having 34 base characters (with a vowel ኧ /ə/), each of which are modified to have six other orders representing vowels in the order of ኡ /u/, ኢ /i/, ኣ /a/, ኤ /e/, እ /ɨ/, and ኦ /o/.

Like other Semitic languages, complex morphological processes are carried out on Amharic word classes such as verbs, nouns and adjectives [START_REF] Yimam | Yamarignasewasiw (Amharic grammar)[END_REF]. Amharic verbs are the most complex word classes and can be generatedby attaching affixeson verbal stems. On the other hand, verbal stems can be generatedfrom verbal roots by inserting vowels between radicals. For example, the verbal stem ገደል-/gədəl-/ is derived from the verbal root ግ-ድ-ል/g-d-l/. Moreover, verbal stems (e.g. ተገደል-/təgədəl-/) can be derived from other verbal stems (e.g. ገደል-/gədəl-/) by affixing morphemes. The verb formation process is usually completed by attaching a verbal stem with person, gender, number, case, tense/aspect and mood markers. For example, from the verbal stem ገደል-/gədəl-/ the following verbs can be generated: ገደልኩ/gədəlku'I killed'/, ገደልኩህ/gədəlkuh'I killed you'/, ገደልን/gədəln'we kill'/, ተገደልኩ/təgədəlku'I was killed'/, ገደለች/gədələtʃ'she killed'/, etc. As verbs are marked for subject and object, they alone can representa completesentence.For example, the word አልሰበረንም/ʔəlsəbərənɨm'he did not break us'/, which is constructed from the morphemes ʔəl-səbər-ə-nɨ-m, is a complete sentence with the following linguistic information:ʔəl-…-m /not/, -səbər-/did break/, -ə-/he/ and -nɨ-/us/. Accordingly, thousands of verbs can be derived from a verbal root through a complex morphological process carried out by attaching a combination of person, case, gender, number, tense, aspect, mood and others (Abate and Assabie,2014;[START_REF] Assabie | Development of Amharic morphological analyzer[END_REF].

Based on a morphological structure, Amharic nouns and adjectives can be either derived or nonderived. For example, the word መሬት/məret'earth'/ and ዛፍ/zaf'tree'/ are non-derived nouns whereas words like ስብራት /sɨbɨrat'the state of being broken'/ and ደግነት/dəgɨnət 'generosity'/ are nouns derived from the verbal root ስ-ብ-ር/s-b-r 'to break'/ and the adjective ደግ/dəg 'generous'/, respectively. Derived nouns are generated from other word classes though morphological processes. In general, Amharic nouns can be derived from verbal roots, adjectives and other nouns by affixing vowels or bound morphemes. Derived adjectives can be formed from verbal roots by infixing vowels between consonants (e.g. ክ-ብ-ድ/k-b-d 'to become heavy'/ → ከባድ/kəbad 'heavy'/), nouns by suffixing bound morphemes such as -ኧኛ /ʔəɲa/ (e.g. ጉልበት/gulbət 'power'/ → ጉልበተኛ/gulbətəɲa 'powerful') and verbal stems by prefixing or suffixing bound morphemes (e.g. ደካም-/dəkam-/ → ደካማ/dəkama 'weak'/). Although the morphological process of derivation of nouns and adjectives is complex by itself, even more complexity arises from their inflections. Amharic nouns and adjectives are inflected for number by suffixing -ኦች/-ʔotʃ/ or -ዎች/-wotʃ/, definiteness by suffixing -ኡ/-ʔu/ or -ዉ/-wu/, objective case by suffixing -ን/-n/, possessive case by suffixing different morphemes depending on the subject, and gender by suffixing -ኢት/-ʔit/. These inflections can appear alone or in combination at the same time, along with prepositions and negation markers which lead to the generation of thousands of word forms from a single noun or adjective. For example, ያለባለቤቶቹ/jaləbaləbetotʃu 'without the owners of the house'/ is generated from the morphemes jə-ʔələbalə-bet-otʃ-u(jəpreposition 'of/with', ʔəlnegation marker'not/without', baləpossessive marker'owner of', betnoun 'house', otʃ plural marker, and udefinite marker 'the') where the core morpheme is the noun ቤት/bet 'house'/.

RELATED WORK

Semitic languages are known to pose unique challenges in the development of NLP applications due to their complex morphologies. These challenges are propagated to the development of IR systems since the effectiveness of IR systems depends on the availability of various NLP tools and resources. In this section, we discuss the techniques and NLP resources used to develop IR systems for Semitic languages in general.

Arabic is the largest of the Semitic language family. Arabic IR systems have a long history (Ambatiet al., 2008;[START_REF] Larkey | Light stemming for Arabic information retrieval[END_REF]Darwish and Magdy, 2014). For example, Al-Hadidet al. (2014) developed a neural network-based model where documents and queries are represented using stems and their similarity is computed using cosine similarity. [START_REF] Musaid | Arabic information retrieval systembased on morphological analysis (AIRSMA): A comparative study of word, stem, root and morphosemantic methods[END_REF] investigated the effectiveness of word-based, stem-based, and rootbased representation of documents and queries.The word-based and stem-based approaches miss relevant documents while root-based approach retrieves non-relevant documents. [START_REF] Moukdad | A comparison of root and stemming techniques for the retrieval of Arabic documents[END_REF] conducted a resarch to compare the effects of stem and root on Arabic IR. The retrieval effectivness of stem and root were evaluated on search engine. The results of the expermints indicas stemming is more effective than root. [START_REF] Larkey | Light stemming for Arabic information retrieval[END_REF] investigated the effects of light stemming (removal of prefix and suffix) on Arabic IR. A comparasion between stem-based and root-based retrieval was performed. The finding indicates light steming outperforms than root and other stemmers which are based on detail morphological analysis. [START_REF] Abdusalam | Effective retrievaltechniques for Arabictext[END_REF] presented an Arabic text retrieval technique using lexiconbased light stemming. The studyevaluated the effectiveness of lexicon-based light stemming, Arabic patterns, root, expanding query and filtering foreign words using n-grams. According to the results, the preprocessing techniques like normalization, stopword removal and lightstemming improve retrieval results whereas n-grams and roots decrease the performance. The lexiconbased stemming and the relevance feedback approaches perform better than light-stemming approach alone. [START_REF] Ali | An intelligent use of stemmer and morphology analysis for Arabic information retrieval[END_REF]investigated the effect of morphological analysis on Arabic IR. A rule-based stemmer was used to extract the root/stem of words to be used as indexing and searching terms.

The resultsshowed slight improvement on IR effectiveness due to the stemmer.

Hebrew is one of the Semitic languages spoken mainly in Israel. [START_REF] Carmel | Morphological disambiguation for Hebrew search systems[END_REF] presented a morphological disambiguator based on a statistical approach that takes advantage of an existing morphological analyzer. The approach is context-free and was used for query analysis and linguistic indexing of text documents. Instead of words, the morphological patterns were used for disambiguation. The statistical morphological disambiguator returns only the best base form(s), or lemma(s). It makes the decisions of the most likely set of analyses based on the frequency of the morphological patterns associated with the analyses of the input word. The disambiguator was tested by integrating with the Hebrew search engine. It conflates all inflectional forms andthe performance of the search engine increased. [START_REF] Ornan | A morphological, syntactic and semantic search engine for Hebrew texts[END_REF] designed Hebrew search engine by applying a rulebasedmorphological analysis. The design of the search engine takes into account the construction of a morphological, syntactic and semantics analyzer. The search engine eliminates words unsuited both to the syntax and the semantic of a sentence.

Although Amharic is significantly used in Ethiopia, the status of IR system development for the language is relatively at rudimentary level. [START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF] studied the retrieval effectiveness of word-based, stem-based, and rootbased approaches on Amharic language. The experiments were carried out by running 40 queries on 548 documents using OKAPI systemand the study concludes that stem-based retrieval is slightly better than root-based. Similarly, Mindayeet al.

(2010) developed an Amharic search engine using stems. The system was tested with11 queries on 75 news documents. The average precision and recall values were 0.65 and 0.95, respectively using OR operatorin between query terms, and 0.99 and 0.52, respectively for AND operator. Argawet al. (2004) developed dictionary-based Amharic-English IR system. Documents and queries were represented using Bag-Of-Words (BOW). Stopwords were removed using Inverse Document Frequency (IDF) and stopwordslist. The average precisions of 0.3615 and 0.4009 were achieved using IDF and stopword list, respectively. [START_REF] Argaw | Dictionary-based Amharic-French information retrieval[END_REF] build dictionary-based Amharic-French IR system with and without word sense discrimination using BOW approach.Stemming was applied to remove prefix and suffix. The experiments were conducted on SICS and Lucene search engines. Stopwords were removed by using IDF. While there are several studies that focused on the development of IR systems for Semitic languages, most of them have followed the techniques employed for morphologically simple languages like English. This has not produced the desired retrieval result as documentscould not be represented appropriately. Only fewstudies have tried to consider the issue of document representation in a systematic way. In our approach, we address this crucial issue of document representation in the development of Amharic IR.

DESIGN OF AMHARIC IR

The main objective of this work is to systematically identify the optimal representations for documents (and queries) in Amharic IR. It focuses on the selection of the structures of terms and stopwords based on the morphological characteristics of the language. Taking these issues intoaccount, we also propose an Amharic retrieval system which is slightly different from the basic architecture of IR systems. In our case, stopwords are removed after the application of morphological analysis on documents and queries as shown in Figure 1.Both documents and queries pass through the same preprocessingtasks that involve language specific tokenization,character normalization, and removalof punctuationmarks. Text preprocessing is followed by morphological analysis which is performed on documents and queries to select appropriate terms for document representation. Morphological analysis is among the key tasks in our IR system as it helps to remove stopwords from documents and queries.

Preprocessing

Preprocessing includes tags removal, tokenization, character normalization and punctuation mark removal.Tokenization is done using space andpunctuationmarks as delimiters of words.Character normalizationis made to represent various characters having similar pronunciation using a single grapheme.Base characters having such property are {ሀ /hə/, ሐ /hə/, ኀ /hə/ and ኸ /hə/}, {ሠ /sə/ and ሰ /sə/}, {ጸ /ts'ə/ and ፀ /ts'ə/}, and {አ /ʔə/ and ዐ /ʔə/}.Furthermore, the fourth orders characters {ሃ /ha/, ሓ /ha/, ኃ /ha/ and ኻ /ha/} and {ኣ/ʔa/ and ዓ/ʔa/} have similarity phonemes with the correspondence base character.Therefore, Amharic character normalizationinvolves mapping of ሀ /ha/,ሃ /ha/, ሐ /ha/, ሓ /ha/, ኃ /ha/,and ኃ /ha/toሀ/ha/; አ /ʔa/, ኣ/ʔa/, ዐ /ʔa/,and ዓ /ʔa/ to አ/ʔa/, etc. For example, the word ስለጸሀፊዋ can also be written as ሥለጸሀፊዋ, ስለጸሐፊዋ, ሥለጸሐፊዋ, ሥለጸኀፊዋ, ስለጸኀፊዋ, ሥለጸሐፊዋ,ስለፀሀፊዋ,ስለፀሐፊዋ,ሥለፀኃፊዋ,ስለፀኻፊዋ,etc. although some of them rarely appear in a text.

Morphological Analysis

Documents and user information need should be represented appropriately using terms that will be used later for matching query with document. It is to be noted that indexing terms are weighted based on theword frequency. In IR, most often, the variants of a word are conflated during indexing into a single form.It has the advantage of making the calculation of indexing term frequency straightforward. Therefore, in this research,we study the feasibility of stem-based and root-baseddocument representation with respect to their effectiveness for Amharic IR. Since well-designed Amharic morphological analyzer is not available, we design semi-automatic annotationto morphologically segment words in documents.

Stem-Based Morphological Analysis

One of the bases of Amharic words are stems. A large number of words are formed by attaching affixes to stems. Therefore, morphological analysis should be carried out to extract the stem from the rest of morphemes. For example, the morphological structures of the primary noun በመንገዶቻችን/bəməngədocatʃɨn'by our roads'/and the adjective የደጋጎች/jədəgagotʃ'of generous'/are shown as follows.

በመንገዶቻችን

የደጋጎች በ_መንገድ_ኦች_አችን የ_ደግ_ኦች pre-stem-pl-1,pl 1 gen-stem-pl by-road-many-our of-generous-many

Similarly, variants of adjectives and nouns derived from primary nouns are mapped into their common stems of nouns. For example, the derived noun ስለልጅነቴና/sɨləlɨdʒɨnətena'and about my childhood'/ and the derived adjective ዓለማዊ/ʔələmawi'worldly'/ are morphologically segmented as follows.

ስለልጅነቴና

ዓለማዊ ስለ_ልጅ_ነት_ኤ_ና ዓለም_አዊ pre-stem-nom-1,s-con sem-adj about-child-being-my-and world-suf Amharic verbs undergo complex morphological process. Verbs are marked for person, gender, number, tense, subject, object, and negation by attaching a series of affixes. For example, the word ፈለገቻችሁ/fələgətʃatʃɨhu'she wanted you'/and the verb ከአልተስማማናቸውም/kəʔəltəsmamanatʃəwɨm'and if we are not comfortable for them'/ is analysed as follows.

ፈለገቻችሁ ከአልተስማማናቸውም ፈለግ_ኧች_አችሁ

ከ_አል _ስማም_ን_አቸው_ም

1 1: first person,2:second person, 3: third person, s:singular,pl:plural,pre:preposition,suf:suffix, nom:nominative,con:conjunction,neg:negative, gen:genitive, def: definite marker, adj: adjectivizer, sub: subject, obj: object stem-sub-obj pre-neg-stem-1,pl-3,pl-neg search-she-you from-not-comfort-we-they-not Amharic has both basic and derived (causative, passive, infinitive and reduplicative) types of verbal stems.Causative stems are formed using the prefixes አ-/ʔə-/ and አስ-/ʔəs-/ whereas passive stems are formed using the prefix ተ-/tə-/. Infinitive stems are also formed using the prefixመ-/mə-/ and reduplicative stems are formed by duplicating the middle consonant. For example, the wordsከተሰበረ/kətəsəbərə'if it is broken'/ and ሰባበራችሁ/səbabəratʃɨhu'yourepeatedly broke'/ have the derived stems ተሰበር-/təsəbər-/ and ሰባበር-/səbabər-/, respectively, but a common basicstemሰበር-/səbər-/. From the semantics point of view, there is no conceptual difference between derived and basicstems. Moreover, the derivedstems are generated from basicstems which are common forms for many variants than derivedstems. Therefore, in the case of stem-based indexing and retrieval, variants of a word are represented using their basicstems.

Stemming is usually applied since lemmatization is more computationally consuming for just slight effectiveness improvements [START_REF] Balakrishnan | Stemming and Lemmatization: A Comparison of Retrieval Performances[END_REF]. Stemming has also been applied in Amharic IR systems (Mindayeetal., 2010;[START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF].However, stemming is not expected to produce the desired result of term frequency in Amharic text. Morphological variants of Amharic verbs can have more than one stem. For example, morphological variants such as ሰበረ /səbərə'he broke'/, ተሰባሪ /təsəbari'broken'/, and አሳበረ/ʔəsabərə'he helped to break'/ have the basic stems ሰበር-/səbər-/, ሰባር-/səbar-/ and ሳበር-/sabər-/, respectively. As a result, stemming provides distorted frequency since each stem of variants is counted differently though they are semantically similar. Therefore, Amharic verbal stems need one more reduction analysis to extract root. Indeed, verbal stems are themselves formed from roots.

Root-BasedMorphological Analysis

Roots are the basis for the formation of basic stems and many other variants of the same Amharic word. Though some words are derived from stems, their origins are roots rather than stems.For example, the morphological structures of the verb ከሰበርኳቸው /kəsəbərk w atʃəw'if I break them'/,the derived noun ስብራቴ /sɨbɨrate'my brokenbelonging'/ are as follows.

ከሰበርኳቸው ስብራቴ ከ_ሰበር_ኩ_አቸው ስብር_አት_ኤ pre-stem-1,s-3,pl stem-nom-1s ከ_ስ-ብ-ር_ኩ_አቸው ስ-ብ-ር_አት_ኤ pre-root-1,ps-3,pl root-nom-1ps from-break-I-them break-nom-my

In the above example, stems of the two words have different forms while they have the same root i.e., ስ-ብ-ር/s-b-r/.According to [START_REF] Yimam | Yamarignasewasiw (Amharic grammar)[END_REF], more than 10 basicstems can be generatedfrom a given root. As depicted in Table 1, variants of words describe similarconcept, but they have different stems. This affects the term frequency which has an impact on ranking and retrieval of documents. On the other hand, all variants have a single root. Therefore, rootbasedrepresentation maps morphologically related words into one common form. Accordingly, statistics information based on root-based approach can be computed accurately so that the actual term frequency can be known.The actual number of variants will be equal to the frequency of their common root. However, the frequency of each stem will be less than the actual occurrence of variants. Moreover, root form increases the matching possibilities between query terms and index terms. Thus, the root-based approach becomes a better way to represent documents and queries for Amharic IR.We have also experimentally analyzed the viabilities of stem-based and root-basedapproaches.

Stopword Removal

Stopwordsare words thatevenly occurin many documents and serve as purpose rather than content. Thus, they are removed from documents and query.Stopwords can be removed either by applying a list or IDF. In morphologically simple languages like English, stopword identification and removal is achieved by considering a list of words that are identified to be stopwords. The conventional trend applied so far for removing Amharic stopwords is also to use a list. However, taking the characteristics of language into consideration,this is certainly not the most appropriate way. Indeed, Amharic stopwords:(i) do notnecessarily exist as standalone words; (ii) can accept prefixes and suffixes; and (iii) mayexist as part of Amharic words and serve as prefix or suffix. For these reasons, it is not possible to find and remove all Amharic stopwords unless the morphological structure of words is known. For example, one may consider words like "the" a stopword in English. Its Amharic equivalent is a suffix "-ኡ/-u/" or "-ው/-w/" that does not appear as a standalone word. Accordingly, "the house" and "the student", for instance, are equivalent to In both cases,itis shown that stopwords significantly impact on retrieval effectiveness, size of index, and term weighting of non-stopwords. Experimental results also showed that the rootbased approach is better than the stem-based approach in conflating all variants of a stopword.The identified stopwordsinclude prepositions (e.g.ወደ/wədə'to'/, ስለ/sɨlə'about'/, እስከ/ʔɨskə'up to', በ-/bə-'by'/, ከ-/kə-'from'/, etc.), conjunctions (e.g.እና/ʔɨna'and'/, ይሁንእንጅ/yɨhunʔɨndʒɨ'however'/, እዚህ/ʔɨzih'here'/, etc.), negation markers (አል…ም/ʔəl…m'not'/), indefinite articles (አንድ/ʔənd'an'/), auxiliary verbs (እ-ል/ʔ-l'say'/, ን-ብ-ር/n-b-r'was'/, etc.), ወዘተ/wəzətə'and so on'/, etc.

Indexing

To test the effect of morphological analysis on Amharic IR,stem-based and root-based indexes are created using Lemur toolkit. Lemur is also used for retrieval purpose. The stem-based index was created using the basicstems of words while the root-based index was created using the root of words.

Matching

In the proposed system, document processing involves text preprocessing, morphological analysis, stopword removal and indexing. As a result of this process, we obtain indexed documents. On the query side, we apply similar processes except indexing. Thus, query processing provides a set of terms representing information needs of users. Searching of relevant documents is carried out by matching query terms (representing information need of users) with index terms (representing documents). In this work, we use exact vocabulary term matching. It searches documents that contain the query terms without analysing the semantics of words and without considering the semantic connections between them.

Ranking

For a given query Q and the collection of retrieved documents D, the Lemur toolkit ranked retrieval results based on their relevance. The document length and number of matching query terms are considered. OKAPI ranks documents based on the following algorithm. Where d is document, w is word, p(w|θQ) is a query model, is estimatedquery, p(w|C) is the collection language model, ps(w|d) is the smoothedprobability of a word seen in the document, αd is a coefficient controlling.

EXPERIMENT

Experimental Data

The experimental setup followed the format of the

Implementation and Measures

Python was used for the preprocessing tasks while indexing and retrieval were performed using Lemur toolkit, which is a search engine designed to support research on language model (LM) for IR tasks2 . The retrieval effectiveness was evaluated automatically using trec_eval tool which can compute many evaluation measures3 . LM and BM25 models were used as retrieval models.

Results and Discussion

Results

To investigate the effectiveness of Amharic system with respect to various word forms used for document representation, we conducted three retrievalexperiments: word-based, stem-based, and root-based retrieval. The retrieval effectiveness is shown is Table 2. The root-based approach retrieves more relevantdocuments than stem-based and wordbasedapproaches. It has also rejected non-relevant documents better than stem-based and word-based approaches. The word-based and stem-based methods miss more relevant documents since they cannot handle some morphological variations. The retrieval effectiveness of the three approaches decreases from precision @5 documents to precision @20 due to scarcity of relevant documents in the test collection.

Discussion

Comparison of root and stem for retrieval Although the stem-based approach could not conflate all variants, it improves retrieval effectiveness to some extent. However, it affects the actual term frequency of some word classes which results in loss of the rank of retrieved relevant documents.Some relevant documents which are not retrieved withstem-based approach are retrieved using root-basedretrieval. Furthermore, some non-relevant documents retrieved in the case of stem-based approach are not retrieved with root-basedapproach. There are three reasons behind this.

First, root can conflate all morphologically variants to one common form, but not stem. For example, the stems of variants ሰበረ/səbərə/, ስበር/sɨbər/, ስብራት/sɨbɨrat/,አሳበረ/ʔəsabərə/, ሰባራ/səbara/, አሳብራት/ʔəsabrat/, ተሰብሮ/təsəbro/ are ሰበር-/səbər-/, ስበር-/sɨbər-/, ስብር-/sɨbɨr/, ሳባር-/sabar-/, ሰባር-/səbar/, ሳብር-/sabr-/, and ሰብር-/səbr-/, respectively. This creates termmismatch with each other. However, all variants have one common root ስ-ብ-ር/s-b-r/.Therefore, the root-based representation increases the term frequency which usually leads to better retrieval result.

Second, root forms donot conflate semantically unrelated words to a common form. However, the stem-based approach sometimes conflates semantically unrelated words. For example, ገደል/gədəl/ is the stem of the verb ገደለ/gədələ'he killed'/ and the noun ገደሎች/gədəlotʃ'cracks'/. However, their roots are ግ-ድ-ል /g-d-l/ andገደል/gədəl/,respectively.The verb ሲገድል/sigədɨl'as he kills'/ and the noun ገድል/gədɨl'contending'/ have the same stem ገድል/gədɨl/. However, their roots are ግ-ድ-ል /g-d-l/ and ገድል/gədɨl/, respectively. This indicates that the use of stemsleads to retrieval of non-relevant documents. Therefore, the stem-based approach is not powerful to filter out non-relevant documents.

Third, the retrieval result of the stem-based approach depends on the query word variants while this is not the case with the rootbasedapproach. The root-based approach performs equally for all the variants of the query terms. However, the stem-based approach returns different results in different ranks. In Amharic, different users will certainly construct the same information need using different word variants. For example, the query 'the causes of air pollution' can be constructed as:

▪ የአየርንብረትብክለትመንስኤወች/jəʔəjərnɨbrətbɨklət mənsɨʔewətʃ/;

▪ ለአየርንብረትመበከልመንስኤወች/ləʔəjərnɨbrətməbə kəlmənsɨʔewətʃ/; ▪ የአየርንብረትበካይመንስኤወች/jəʔəjərnɨbrətbəkajm ənsɨʔewətʃ/; etc. After the stem-based morphological analysis, all the three queries will have same query terms (አየር, ንብረት and መንስኤ) except one (ብክል, በከል and በካይ). As a result of variation of the thirdterm, the system returns different results in different ranks. Therefore, stem-based approach performs differently for the same test collection (see Figure 3). The top line (in green) depicts rootbasedretrieval whereas the remaining two lines representstem-based retrieval.Comparison with previous studies In the previous studies, a few Amharic IR systemshave been evaluated. Some of them are based on stems (Mindayeet al., 2010;[START_REF] Munye | Amharic-English bilingual web search engine[END_REF] while some others are based on citation forms (Argawet al., 2004) and root-based [START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF]. However, due to the complexity of the language the stem-based and n-gram models do not work well. In this work, we have shown that the rootsaremore powerful for Amharic IR than stems. This is a new finding which was not looked in the previous researches. Other authors suggestedstem-based as the best option. [START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF] investigated the effects of the stem and root-based approaches on Amharic IR. Their finding was that the stembased approach is better than the root-based one. The justification was that many Amharic words have common root though they are semantically unrelated. Their report states that stem-based improves the root-based method in terms of rejecting irrelevant documents that could be retrieved by the use of roots. However, their experiment was carried out on wrong representation of roots. For example, the root of the word ዝናብ/zɨnab 'rain'/ is misrepresented asዝንብwhich is the same as ዝንብ /zɨnb'fly'/. However, the correct root representation is ዝ-ን-ብ /z-n-b/. Another problem in their approach is that they used root representation for any types of words by removing vowels in non-derived words as well. This method conflates many semantically unrelated words. Furthermore, their system wrongly extracts the root radicals of some words. For example,ምትis considered asthe root of the words ሞተ/motə'die'/ and መታ/məta'hit'/. But, their roots are ም ው -ት and ም-ት, respectively. Such cases are prevalent in Amharic. Thus, previous studies that recommended the use of stems made their conclusions without through investigation on the applicability of roots.

Comparison with Google Amharic retrieval

The Google Amharic search engine is based on stem. It returns different retrieval results in different ranks for the same query using different variants of query words. Similar results are obtained inour work while we apply stem-based approach. For example, Google search results of the queries የአጥንትስብራት/jəʔət'ɨntsɨbɨrat'being broken bone'/ and የአጥንትመሰበር/jəʔət'ɨntməsəbər'the process of being broken bone'/ are different though the same concept is expressed via different variants. Our approach differs from Google search engine into two ways.

i. Google searches based on both basicstems and derived stems. It returns different retrieval results for basic stems and derived stems queries though they are semantically similar. However, in our work, the stem-based approach is based on basic stems only, providing the same retrieval results for both basic stems and derived stems. ii. Google does not employ roots to represent verbs and words derived from them. However, we use root-based approach as itconflates all variants of words to a common form.

Comparison of LM and BM25

We also compared the performance of LM and BM25. Although LM is very popular and powerful for IR in different languages, previous Amharic IR researches were made based on classical IR models such as vector space model (Mindayeet al. 2010;Argawet al., 2004;Argawet al., 2006). Language modelling was not employed for Amharic IR. In this study, the impact of language modelling retrieval on Amharic IR is also investigated.As depicted in Figure 4,the blue line representing LM is above the red line representing BM25. Both precision and recall values of LM are better than that of BM25 at different levels. This is because of the capability of LM to capture the dependency of words and estimate the probability distribution of a query in each document. This means LM is a more suitable retrieval model for Amharic language.The roots of verbs and words derived from them and the basic-stems of other word classes are robust to represent words not only in IRbut also in other applications such as machine translation, information extraction, sentiment analysis, etc. The development of Amharic IR demands thorough investigation of the characteristics of the language. Its complex morphology affects the way documents and queries are represented for the task of information retrieval. In this work, we conducted several experiments using various forms of words for document representation. Experimental results have shown that root forms of words provide better results in representing documents. It is also shown that, with the use of root forms, the LM retrieval is better than BM25 model. As the proposed system is based on exact vocabulary term matching, future work needs to consider query expansion so as to take into account synonyms, collocation words, name identification, etc.Furthermore, the language has many ambiguous words which have different meaning in various contexts. Thus, this work may be improved by handling ambiguity.

Figure 1 :

 1 Figure 1: Basic architecture of the proposed Amharic IR system.

Score

  qi,D) is qi's term frequency in the document D, |D| is the length of the document D in words, and avgdl is the average document length in the text collection from which documents are drawn. k1 and b are free parameters. In this research the value of k1 = 2.0 and b = 0.75. IDF(qi) is the IDF (inverse document frequency) weight of the query term qi. In case of LM, the KL divergence ranking function with default parameters was used. It captures the term occurrence distributions and computed as follow. (𝑤,𝑑)>0,𝑝(𝑤|θQ)>0 + 𝑙𝑜𝑔α𝑑 (2)

Figure 2 :

 2 Figure 2: Recall-precision-curveof stem and root. The recall and precision values of stem-based and root-based approaches are shown in Figure 2. The blue line depicts the root-based retrieval effectiveness, whereas the red line represents the stem-based retrieval results. It can seen that the retrieval effectiveness of root-based approach outperforms stem-based approach.

Figure 3 :

 3 Figure 3: Recall-Precision curves based onstemsand roots.

Figure 4 :

 4 Figure 4: Comparison of LM and BM25 retrieval models on root-based approach.

  The result of SICS is better

	than Lucene. The word sense descrimination
	performs slightly better than non discrimination.
	Document Processing	Documents	Preprocessing	Morphological Analysis	Stopword Removal	Indexing	Index
								Matching	Ranking
	QueryProc	essing	Query	Preprocessing	Morphological Analysis	Stopword Removal		QueryTerms
			User interface				Rankeddocuments

Table 1 :

 1 Amharic root and stems of variants.

	there could

ቤቱ /betu/(ቤት /bet/ + -ኡ/-u/)and ተማሪው/təmariw/ (ተማሪ/təmari/ + -ው/-w/), respectively. As

Table 2 :

 2 Retrieval effectiveness based on the three approaches.

	Approach	Precision P@5 P@10 P@15 P@20 MAP
	Word	0.56	0.49 0.44	0.40 0.43
	Stem	0.62	0.53 0.47	0.43 0.57
	Root	0.79	0.70 0.61	0.55 0.70

http://www.lemurproject.org

http://trec.nist.gov/trec_eval


Aimery Auxéméry

Gilles Philippot

Matthew R Suchomel

Denis Testemale

Cyril Aymonier

Stabilization of tetragonal zirconia nanocrystallites using an original supercritical-based synthesis route

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction:

Zirconium dioxide (ZrO2) has been of major industrial and scientific interest over the past decades for its wide range of applications, in particular thanks to its catalytic properties 1 . Regardless the multiple synthesis routes employed such as: precipitation of metal salts 2 ; atomic layer deposition 3 ; ball-milling 4 ; chemical vapor deposition 5 ; radio frequency sputtering 6 ; etc., undoped bulk ZrO2 is stabilized in the monoclinic structure (m-ZrO2) at ambient temperature and atmospheric pressure. There, the small cationic size of Zr 4+ covalently bonds to oxygen anions in a sevenfold coordination environment. However, improved catalytic properties are observed in tetragonal zirconia (t-ZrO2) where Zr 4+ is coordinated with eight oxygen anions. To stabilize this allotrope under mild temperature and pressure conditions, it generally necessitates the use of iso-or aliovalent zirconium substitution of larger cationic radii 7,8 . For example, many works showed stabilized t-ZrO2 through Y 4+ , Ga 3+ , Gd 3+ , Fe 3+ substitution [9][10][11] . There, three main stabilization mechanisms are proposed: (i) the generation of structural defects such as lattice oxygen ion vacancies (L.O.I.V.) inducing structural variations in ZrO2 formation by changing zirconium coordination, from seven to eight-fold, (ii) the relieve of the internal strain associated with the dilatation of the network due to the substitution of Zr 4+ with an oversized cation favoring the eightfold oxygen coordination and (iii) the combination of both mechanisms at the nanoscale 12 .

It is also possible to stabilize t-ZrO2 without cationic substitution. To do so, three main effects are identified in the literature: (i) the presence of oxygen vacancies, (ii) the existence of structural similarities and (iii) the size effect.

(i) The influence of hydroxyl defects was highlighted with hydrothermal syntheses by adjusting the appropriate pH 13,14 . While low pH conditions (pH < 3) generally enable stabilization of m-ZrO2, high pH conditions (pH > 9) favor the formation and stabilization of t-ZrO2. The stabilization mechanism of m-ZrO2 at low pH is believed to be due to the presence of Zr(OH)2 2+ clusters prior to nucleation, whereas at higher pH conditions, the increasing presence of Zr(OH)5 -clusters allows the entrapment of hydroxyl groups inside the structure, thus triggering the nucleation and favoring the growth of t-ZrO2 14,15 .

(ii) The existence of structural similarities was observed in the case of sol-gel syntheses using in situ X-ray scattering and Raman spectroscopy during crystallization of the gel [16][17][18][19] . This clearly revealed the existence of similarities (e.g., bond lengths) between the amorphous phase and the intermediate metastable t-ZrO2 crystalline structure. Also, increasing annealing temperatures revealed that the stabilized metastable t-ZrO2 structure was only an intermediate to the thermodynamically stable m-ZrO2, as t-ZrO2 inevitably transformed to m-ZrO2 at ≈ 600°C, far below the thermodynamic limit (≈1205°C).

(iii) The concept of a size effect in the stabilization of t-ZrO2 was first demonstrated in 1964 20 . Subsequently, numerous works have focused on identifying the threshold size for stabilizing t-ZrO2 without dopants. Depending on the studies, sizes ranging from 2 to 30 nm [20][21][22][23][24] were found to be "critical" to stabilize t-ZrO2 25 .

In 2005 many works were reviewed in a multimodel study focusing not only on the role of size effect, but also considering the influence of oxygen vacancies and structural similarities in the stabilization process for the whole 2 to 30 nm size range 26 . These same concepts have also recently been transposed to the stabilization of metastable c-ZrO2 in ultrafine nanoparticles exhibiting sizes smaller than 2 nm 27 . However, the absence of a consensus regarding stabilization mechanisms in ultrafine t-ZrO2 nanoparticles has led to propose that stabilization is system dependent. 19,24,40 , the synthesis procedure is nevertheless long and generally requires the use of hazardous organic solvents such as oleylamine, trioctylphosphine oxide (TOPO) 24,36 etc. In comparison the supercritical fluid technology which has been intensely developed over the past decades to circumvent the problem of environmental considerations and use of hazardous reactants appears to be an attractive alternative. In a supercritical approach, the solvent properties such as solvation ability, specific diffusivity, density and viscosity are modified, enabling nanoparticle crystallization in very short synthesis times with an excellent control over structure, morphology, size, and size distribution [41][42][43][44][45] . The technique has been used to synthesize ultrafine ZrO2 nanoparticles in both supercritical water and hydro-alcoholic mixtures. In these cases, the solvent choice is crucial; it is reported that the use of water lead to the synthesis of monoclinic nanoparticles [46][47][48][49] , while using hydro-alcoholic mixtures, the produces particles exhibiting a mixture of monoclinic and tetragonal phases 38,46,50,51 .

Based on this observation, the current work reported here studies the influence of water quantity in the media to synthesize t-ZrO2 nanoparticles. Specifically, we examine for the first time the synthesis of ZrO2 nanocrystals in supercritical fluids using borderline non-hydrolytic sol-gel (B.N.H.S.G.) conditions. The term borderline is used to nuance from N.H.S.G. reactions when small amounts of water molecules are generated in situ from solvent decomposition to trigger hydrolysis/condensation reactions with metal precursors and the formation of oxo bridges prior to crystallization 36,52,53 . Playing on the experimental parameters we are able to fine tune the quantity of water generated in situ from the dehydration reaction of alcohols. In this context we chose zirconium propoxide precursors using ethanol (pC = 6.27MPa, TC = 241.56°C) as solvent, because the amount of water that can be generated upon pressure and temperature conditions has been quantified and reported in the literature 54 . Rapid crystallization of ZrO2 was realized in a short reaction time (tR = 30s) during simple single-step syntheses without re-quiring further annealing to obtain the final nanoparticles. Experiments were performed in supercritical water, in water ethanol 50 / 50 wt. % mixture 55,56 and in absolute ethanol with the aim to clarify the role of water in the formation of t-ZrO2 nanoparticles. The size effect and more precisely the existence of a critical size below which only t-ZrO2 is produced was also investigated by exploring the additional parameters of precursor concentration and the use of a surfactant. Indeed, the precursor concentration was investigated to optimize supersaturation conditions during the synthesis, with the goal of reducing the nanoparticle size. The influence of a carboxylic acid surfactant (hexanoic acid) was also studied to quench the nanoparticle growth. Following synthesis, the resulting powders were characterized by multiple probes (detailed below) in order to evaluate the effectiveness of varying conditions, and to compare the resulting crystalline polymorph types, fractions, and domain sizes at both local and long-range average length scales.

Materials and method:

Materials:

Zirconium(IV) oxynitrate hydrate (Sigma-Aldrich, ≥ 99%), Zirconium(IV) propoxide solution, 70 wt. % in 1-propanol (Sigma-Aldrich), hexanoic acid (Sigma-Aldrich, ≥ 99%), absolute ethanol (Sharlau, ≥ 99.9%) and de-ionized water were used as received.

Apparatus and procedure:

ZrO2 nanoparticles were synthesized using a continuous flow reactor designed to study how the water media content and precursor solution concentration influence the final structures. The influence of surfactants on the final crystallite structures and particle sizes was also evaluated. Zirconium(IV) propoxide solutions were prepared inside a glovebox under continuous argon flow to prevent zirconium precursors from reacting with air moisture. The solutions were then placed under magnetic stirring and argon flow during the syntheses to prevent contamination of the precursors. Nitrate precursors, which are not sensitive to air moisture were slowly dissolved in de-ionized water by moderate stirring under standard air atmosphere.

As presented in Figure I, JASCO PU-2080 PLUS high-pressure pumps (HPP) were used to feed the reactor with the precursor solutions in the main injection line. Preheated water or ethanol could also be injected using the secondary line to play on the amount of water in the reactor. The hotzone reactor consisted of a coiled 5 m long 1/8'' SS-316 stainless steel tubing with an inner diameter of 1.58 mm, for a total volume VR = 8 cm 3 , inserted inside a heating clamp to control the desired reaction temperature (400 ± 2°C). Thermal quenching of nanoparticle growth was realized by flowing the nanoparticle suspension through an ice-cooled 1/8'' SS-316 stainless steel tubing downstream the reactor prior recover the powder and solvent at the outlet. The reactor was held at a constant pressure of 25 MPa using a TESCOM ® pressure-regulator valve (PRV).

Synthesis of Zirconium dioxide nanoparticles:

To evaluate the impact of water on the obtained structural mixtures, the following syntheses were realized: ZrO2-01 was synthesized from nitrate precursors in supercritical water. The second injection line was added to the reactor for the synthesis of ZrO2-02 and ZrO2-03 powders where either preheated water or ethanol were mixed to the zirconium propoxide precursor solution a few cm only from the reactor entrance to allow condensation of precursors prior to crystallization. The secondary line used to inject the desired volume of preheated water or ethanol was held at 25 MPa and 225 °C, while the temperature at the mixing point was maintained at 150 °C. Note that the main injection line was not preheated to avoid side reactions. ZrO2-02 was synthesized from zirconium propoxide precursor in a supercritical 50/50 water/ethanol weight ratio mixture using the two injection lines; and ZrO2-03 with the propoxide precursor in a supercritical mixture of ethanol and preheated ethanol using the two injection lines where water was generated in situ in the secondary line and in the main reactor after mixing. All three syntheses were performed with a precursor concentration [Zr 4+ ] = 10 -2 mol/L. ZrO2-04 was synthesized with the same zirconium propoxide precursor concentration but only using the main injection line, thus reducing further the quantity of water generated in situ. Similarly, ZrO2-05, 06, 07 and 08 were all synthesized from zirconium propoxide precursors in supercritical ethanol using only one injection line, varying the precursor concentration (ZrO2-04 to ZrO2-06) or adding hexanoic acid (HA) as surfactant (ZrO2-07 and ZrO2-08).

To follow the influence of Zr 4+ concentration, syntheses of ZrO2-04 (10 -2 mol/L), ZrO2-05 (3.3 10 -2 mol/L), and ZrO2-06 (6.6 10 -2 mol/L) were conducted in the same experimental conditions (25 MPa, 400 °C, 30 s). Finally, the influence of surfactant addition was observed by varying [Zr 4+ ]:[HA] molar ratios on ZrO2-05 (1:0), ZrO2-07 (1:6) and ZrO2-08 (1:12) with [Zr 4+ ] = 3.33 10 -2 mol/L. The residence time (tR) for each synthesis was fixed to 30 s determined with Equation 1,

𝑸 𝑽 = 𝑽 𝑹 * 𝝆 (𝑯𝒑,𝑯𝑻) 𝒕 𝑹 * 𝝆 (𝑯𝒑,𝑹𝑻) (𝟏)
where VR is the reactor total volume, ρ(Hp, HT) and ρ(Hp, RT) are the solvent densities at high pressure, high temperature and high pressure, room temperature, respectively. QV is the total flow rate through the reactor. Values for the solvent densities for both conditions were obtained from the REFPROP software package 57 . All synthesis conditions are displayed in Table 2 along with the results obtained from each ex situ characterization technique. Average crystallite sizes (A.C.S.) and lattice constants were determined from Rietveld refinements of powder X-ray diffraction (PXRD) data, Average particle sizes (A.P.S.) were determined from transmission electron microscopy (TEM), while PXRD and Raman spectroscopy probed polymorph phase proportions.

Characterization techniques:

Powder monoclinic phase was too low, the lattice parameters were fixed to the last refined values.

Room temperature Raman spectra were collected using an XploRA HORIBA Scientific Raman spectrometer with λexc = 532 nm in a backscattering geometry and equipped with an Andor CCD detector. Data were collected with a resolution of 4 cm -1 over the 110-1700 cm -1 domain and were further integrated using the Labspec software.

Fourier transform infra-red (FTIR) spectroscopy was performed to determine the surface functionalization of the synthesized samples on a Brucker equinox 55 spectrophotometer. 32 scans was realized for each sample in the 400 and 4000 cm -1 spectral absorption domain using a global radiation source and the spectral resolution was 4 cm -1 . The nanoparticles were ground and dispersed into a KBr matrix prior to measurements.

Particle size and morphology of the ZrO2 powders were observed using both a TEM-JEOL 2100 (200 keV) and a HRTEM-FEG JEOL 2200FS (200 keV). Digital Micrograph software was used to process the acquired images. Samples were dispersed in ethanol and deposited on copper-carbon grids for analysis.

Extended X-ray absorption fine structure (EXAFS) measurements were performed on ZrO2-05 at the Zr K edge (17.998 keV) at the BM30B -FAME beamline at the European synchrotron radiation source (ESRF) in Grenoble. 59 The beam size was 100x300 μm 2 (FWHM) and the energy range was set between 17.8 keV and 19 keV. Measurements were performed in transmission mode to collect both incident and transmitted photons. Data were fitted using Demeter 0.9.26 software 60 after fitting the EXAFS of a zirconium metal foil to calibrate the E0 on the absorption edge (reference). The amplitude reduction factor (𝑆 0 2 ) was set to 0.77 after fitting data from zirconium metal foil. Pair distribution function (PDF) analysis was performed after collecting high energy (76.7 keV) total X-ray scattering intensities of ZrO2-02, 05 and 06 powders at room temperature inside 1.0 mm diameter glass capillaries loaded with ZrO2 powders at the XPDF (i15-1) beamline (Diamond synchrotron, UK) using the rapid-access program. Differential correlation function D(r) datasets were obtained from the 2D scattering data with the DAWN and GudrunX software packages and fitting of the realspace D(r) data were performed with the TOPAS v6 software package using a dQ_damping() macro term of 0.075 61 . Phase fractions and lattice parameters for m-and t-ZrO2 allotropes and spherical crystallite domain diameter sizes were refined. Based on Rietveld analysis of PXRD data for the samples with equivalent precursor concentrations, the refined weight percentage of m-ZrO2 decreases with a reduction in water molar fraction, from 100% down to 60%, 36 and 19% for the samples ZrO2-01, 02, 03 and 04, respectively. The refined m-ZrO2 weight fraction is then relatively constant (average ≈15 wt. %) for samples ZrO2-05, -06, -07, and -08 despite varying precursor and surfactant (HA) concentrations. The A.C.S. obtained after Rietveld refinements show that m-ZrO2 A.C.S. is a bit smaller for ZrO2-01 (4.2 nm) in comparison with ZrO2-02 and ZrO2-03 where the A.C.S. is around 5 nm. Such difference can be explained by the difference of chemistry (precursor nature) between the first synthesis and the others. The overall trend regarding the tetragonal phase shows an A.C.S. constant decrease from ≈5 nm (ZrO2-02 and ZrO2-03) down to 2.4 nm (ZrO2-07 and ZrO2-08). And is illustrate with the (101)t reflection broadening, visible for samples ZrO2-03 to 08, together with the (101)t reflection shift that generally accompanies decreases in particle size due to the bond distance distortion, related to the increase in L.O.I.V. 46 . Indeed, the a and b lattice parameters of the tetragonal phase are initially constant for ZrO2-02 to ZrO2-05 with a value around 3.603 Å and then progressively increase up to 3.646 Å for ZrO2-08. The lattice parameters c is progressively decreasing from ≈5.180 Å (ZrO2-02 and ZrO2-03) down to 5.034 Å (ZrO2-07 and ZrO2-08). This illustrate a progressive transition towards a more cubic phase which is the one stable at even high temperature than the tetragonal one in the bulk. The lattice parameters of the monoclinic phase are slightly changing for ZrO2-01 to ZrO2-03 but where fixed for the other fits. Indeed, the quantity of the monoclinic phase decreasing, it was more and more difficult to have a converging refinement.

Results and discussion

Fourier transform infra-red spectroscopy (FTIR) measurements were performed to evaluate the effectiveness of surface functionalization with HA over the synthesized ZrO2 nanoparticles (ZrO2-07 and ZrO2-08). In the 1450-1560 cm -1 region, the difference Δ between the antisymmetric (νAS-COO) and symmetric (νS-COO) vibrations of the carboxylate groups (-COO), 1555 and 1455 cm -1 , respectively is the signature of a chelating bidentate type interaction between the carboxylate function and metal oxide surface 62 . The presence of -CO stretching vibrations is also evidenced at the surface of functionalized samples at 1105 and 1085 cm -1 which are also the signature of the HA grafting at the surface of ZrO2. Also, FTIR absorption spectra reveal no significant intensity changes in the vibrational frequencies related to the adsorbed species when the amount of hexanoic acid is increased from 1:6 to 1:12 inside the precursor solution. The main difference is visible on the vibration band at 1085 cm -1 , typical of alkoxide bonding, that disappears for ZrO2-08. This may be related to the fact that surface coverage by carboxylate species increases further, through chelating bidentate type interactions 63 as the amount of hexanoic acid is increased between samples ZrO2-07 and ZrO2-08.

Raman spectroscopy was used for further structural characterization of ZrO2 powders as it is a reliable technique for precise characterization of both local crystal structure and local disorder. Figure IV shows the Raman spectra of ZrO2 powders, with the same color code as for XRD patterns in Figure II. Frequencies for the observed Raman active modes used as a basis for assignment of the peaks were found in the following literature 23,64,65 .

Raman shifts located at 148, 270, 309, 463 and 638 cm -1 are active modes of t-ZrO2 (labelled as T), while those located at 179, 191, 222, 333, 345, 381, 476, 502, 537, 557 and 613 cm -1 are active modes of m-ZrO2 (labelled as M). Integrated areas underneath the peaks at 148 (T), 270 (T), 179 (M) and 191 cm - 1 (M) were used to quantify, for each ZrO2 powder, m-and t-ZrO2 molar fractions using Equations 2 and 3 66 :

𝑋 𝑡 = 𝐼 𝑡 (148 𝑐𝑚 -1 )+𝐼 𝑡 (270 𝑐𝑚 -1 )
𝐼 𝑡 (148 𝑐𝑚 -1 )+𝐼 𝑡 (270 𝑐𝑚 -1 )+ 𝐼 𝑚 (179 𝑐𝑚 -1 )+𝐼 𝑚 (191 𝑐𝑚 -1 )

(2)

𝑋 𝑚 = 1 -𝑋 𝑡 ( 3
)
where Im and It refer to the integrated areas, at given wavenumbers, of m-and t-ZrO2 active modes, respectively and Xm and Xz being m-and t-ZrO2 molar fractions contained inside each powder. Note that this method is estimated to be quantitatively accurate around 5%, moreover, the measurements being local, they were performed at three different locations of the samples and averaged. The obtained weight fractions are listed in Table 2.

As expected, Raman spectrum of ZrO2 synthesized in supercritical water using nitrate precursors (ZrO2-01) exhibits purely the monoclinic structure 49,67 . To understand the importance of water on the reactional pathway during ZrO2 synthesis, ZrO2-02 and 03 were synthesized with decreasing amounts of water available for hydrolysis and condensation of the zirconium propoxide precursor (see Table 2) prior to entering the reactor with a 50/50 wt. % of a water/ethanol mixture and preheated ethanol, respectively resulting in decreasing m-ZrO2 wt. % as listed in Table 2. ZrO2-04, synthesized with the same precursor concentration resulted in even lower m-ZrO2 wt. % as water was produced inside the reactor, unlike for ZrO2-02 and 03. Such mand t-ZrO2 structural mixtures were already observed in a supercritical isopropyl alcohol and water mixture, where water was added to assure complete precursor condensation 38 .

While all ZrO2 powders, except ZrO2-01, reveal the presence m-and t-ZrO2 Raman active modes, they are nevertheless characterized by different weight on structure intensity contributions. For ZrO2-02 to ZrO2-05, t-ZrO2 active modes at 148 and 270 cm -1 increase in intensity relatively to m-ZrO2 modes at 179 and 191 cm -1 . Also, in the 300-400 cm -1 domain, all three m-ZrO2 active mode intensities dramatically decrease while the tetragonal mode at 309 cm -1 increases and broadens. It is important to note the presence of a slight signal at 309 cm -1 in ZrO2-01 (labelled as t in Figure I), probably corresponding to traces of t-ZrO2 in the synthesized powder. The same trend is observed in the 476-620 cm -1 domain, and specially for ZrO2-05 where both modes at 537 and 557 cm -1 have disappeared. On the other hand, for these samples, all five t-ZrO2 Raman mode intensities increase in the order from ZrO2-02 to ZrO2-05, with no exception. However, concerning ZrO2-06, 07 and 08, the major differences are visible on the t-ZrO2 modes at 148 and 270 cm -1 and m-ZrO2 modes at 179 and 191 cm -1 , where a slight increase in m-ZrO2 relatively to t-ZrO2 modes are observed. These intensity variations reveal slight structure proportions variations in the powders. Proportions obtained from Equation 2give ≈ 70, 40, 29 and 11 wt. % m-ZrO2 for ZrO2-02, 03, 04 and 05, respectively and ≈ 12, 15 and 16 wt. % m-ZrO2 for ZrO2-06, 07 and 08, respectively.

Despite slight discrepancies in quantification of m-ZrO2 phase fraction between the PXRD and Raman techniques, the exact same trend is observed for both probes, revealing unequivocally the presence of a mixture of both structures. For the PXRD analysis of t-ZrO2 rich samples, quantification of m-ZrO2 phase content might be considered limited by instrumental signal-tonoise statistics and intrinsic sample peak broadening. However Raman spectroscopy certainly has the ability to clearly discern each structure present in our ultra-fine synthesized nanoparticles. The decreasing proportion of m-ZrO2 is, first of all, believed to be due to the decreasing amounts of water available dictated by the experimental conditions used. Indeed, water mol. % inside the reactor are 100 mol. %, 71 mol. %, 1.9 mol. % and 0.8 mol. % for ZrO2-01, 02, 03 and-04, respectively. Contribution from ethanol dehydration was calculated from works realized on by-products formed in organic solvents and quantified under solvothermal conditions 54 . But also, the varying m-ZrO2 wt. % can be explained by the initial prepolymerization of nuclei with local structures close to m-ZrO2, in mild hydrothermal conditions at the mixing point at 150°C, thus facilitating ZrO7 polyhedra packing 68 when two injection lines are used.

The decrease in m-ZrO2 fraction observed between ZrO2-04, 05 and 06, is believed to be influenced by the increase in precursor concentration with [Zr 4+ ] = 10 -2 , 3.3 10 -2 and 6.6 10 -2 mol/L, respectively and optimization of supersaturation conditions to form stabilized ultrafine ZrO2 nanoparticles. However, the difference in m-ZrO2 content between ZrO2-05 (11 wt. % m-ZrO2) and 06 (12 wt. % m-ZrO2) is insignificant, showing that the optimal supersaturation conditions were met with [Zr 4+ ] = 3.3 10 - 2 mol/L.

For the third set of experiments based on the addition of surfactants to the precursor solution, visual inspection of PXRD patterns do not seem to reveal peak signatures of m-ZrO2 due to the large decrease in intensity of the (11-1)m reflection and the large broadening of the (011)t plane related to the decrease in the t-ZrO2 A.C.S. (≈ 2 -3 nm). But Rietveld refinements realized on these samples show that non-negligible amounts of m-ZrO2 are nevertheless present in the final powders. This result is confirmed by the good quantitate agreement in m-and t-ZrO2 proportions obtained from both Rietveld PXRD refinements and Raman spectroscopy analysis which also seems to reveal that increasing hexanoic acid contents in the precursor solution 2) that t-ZrO2 nanoparticles are smaller than their m-ZrO2 counterparts, which is actually the case for every sample composed of a mixture of m-and t-ZrO2.

These first results show that, while the precursor concentration is increased, m-ZrO2 A.C.S. decreases from ≈ 4 nm down to 3.5 However, changes in concentrations do not significantly change the m-and t-ZrO2 proportions inside the powders. Changing the concentration, while decreasing t-ZrO2 size does not significantly seem to favor its formation in anyway in our experimental conditions, as can be seen on both Rietveld and Raman results (Table 2). ) and more clearly on the histograms, we see a link between the size reduction and increase in t-ZrO2 content inside the powder mixtures. However, in the second row (ZrO2-05 to 08) the trend is not revealed. As in the first row, all A.C.S. are greater than but approaching the smallest critical size limit (≈ 3 nm) reported in literature, so the increase in t-ZrO2 content can be explained by the critical size theory. On the other hand, in the second row, despite the fact that all A.C.S. values (for both t-and m-ZrO2) are at or even below this lowest critical limit of 3 nm (t-ZrO2), the phase fraction of m-ZrO2 stagnates at ≈ 10-15 wt. %. Therefore, as the t-ZrO2 A.C.S. tend to decrease from ZrO2-05 to ZrO2-08, Figure VI clearly emphasizes the fact that t-ZrO2 formation is favored as the nanocrystals size decreases down to an equilibrium limit of ≈ 85-90 wt. % of t-ZrO2. However, reducing the size even down to ≈ 2 nm does not further increase the relative wt. % of t-ZrO2. These results show the limit of the size effect theory on the synthesis of pure t-ZrO2 nanoparticles especially because we can stabilize m-ZrO2 with A.C.S. smaller than 3 nm in the case of ZrO2-08. Figure VI also emphasizes that in our synthesis conditions, and probably in a general manner, t-and m-ZrO2 nuclei are formed concomitantly, however, in situ studies focused specifically on ZrO2 nucleation would be necessary to validate the hypothesis.

PDF analysis of selected samples provides additional details on crystallite domain size and phase fractions. Differential correlation functions D(r) obtained from PDF measurements performed on powders ZrO2-02, 05 and 06 are displayed in Figure VII in the 1.5-50 Å range. Table 3 lists a summary of parameters refined against the D(r) functions, including m-ZrO2 wt. %, tand m-ZrO2 cell volumes, and average crystallite domain size, for the samples ZrO2-O2, 05 and 06. that was used to perform FEFF calculation and fit the experimental data, while for a monoclinic structure two shorter Zr-Zr bond distances are generally observed at 3.34 Å and 3.46 Å. For information, the distances estimated from the PDF data were divided into two contributions the dZr-Zr(I) and dZr-Zr(II) with values of 3.60 ± 0. 01 Å and 3.63 ± 0.01 Å, which is in good agreement with EXAFS. Therefore, the obtained fitting results tend to indicate the existence of an intermediate structure which is not clearly tetragonal, neither monoclinic 25,70,71 . In addition, for very small nanoparticles, the proportion of surface atoms over core ones increases dramatically, in our case, for a 2 nm spherical particle, since the Zr-Zr distance is 3.64 Å, there are approximately only five Zr atoms across the particle diameter, meaning two at the surface and three in the core. Knowing that surface Zr atoms are generally not fully coordinated, this also supports our observation of an intermediate structure, especially regarding the local environment of Zr atoms and its coordination to O atoms. The present work has demonstrated the phase fraction of m-and t-ZrO2 structural polymorphs can be tuned by varying the amounts of water present during syntheses under supercritical conditions. When ZrO2 was synthesized in pure supercritical water, the final nanoparticles exhibited purely the monoclinic structure. However, when the absolute amounts of water were decreased, the percentage of m-ZrO2 decreased relatively to t-ZrO2 in the final powders. The sudden change in structure proportions and stagnation to the minimum observed between ZrO2-04 and 05 was attributed first of all to the pre-polymerization of nuclei with local structures close to m-ZrO2 at the mixing point (150°C), thus facilitating ZrO7 polyhedra packing for powders 01 to 03, and secondly to the surface strain with the particle due to the size effect inducing distortion in the structure.

Conclusion:

The lowest amounts of water achieved in our synthesis conditions were generated in situ from supercritical ethanol dehydration and led to m-and t-ZrO2 nanoparticles with similar A.C.S. of 3.5 and 2.8 nm, respectively and to ≈ 13 wt. % m-ZrO2 in the final powders for a precursor concentration of 6.6 10 -2 mol/L. Also, the use of hexanoic acid as surface modifiers helped to reduce both t-ZrO2 A.C.S. down to 2.4 nm and size distribution, while maintaining a high degree of crystallinity as revealed by HRTEM diffraction images. However, hexanoic acid did not seem to favor t-ZrO2 formation in terms of molar fractions, but did however in terms of number of t-ZrO2 nanoparticles formed which was evidenced in this work and truly interesting in the comprehension of ZrO2 nanoparticle formation, thus clearly emphasizing the simultaneous nucleation of both structures rather than selection of a reactional pathway by decreasing the nanoparticle size. Also, Fourier transform of Zr K edge EXAFS for the sample containing one of the highest percentage of t-ZrO2 based on Raman (ZrO2-05) revealed a tetragonal structure with slightly reduced metal-metal correlations relatively to metal-oxygen correlations, which is representative of nanomaterials containing significant levels of lattice disorder.

While it is possible that some critical size for the stabilization of tetragonal zirconia exists below 2 nm, sensitive high energy X-ray instruments and careful analysis would like be required to confirm. However, our results presented here suggest instead that reaction pathways and the resulting ZrO2 polymorph stabilization depends strongly on structural nucleation, L.O.I.V. concentrations, reaction kinetics and critically, in supercritical conditions, on water releasing rates. Therefore, though the particle size remains an important factor in achieving metastable t-ZrO2 nanoparticles, its stabilization may not result solely from geometric size factor.

Overall, the combination of these analyses confirmed that the control of reaction kinetics through in situ water generation greatly helped to favor t-ZrO2 formation, even if traces of m-ZrO2 were still observed in the final powders.

* cyril.aymonier@icmcb.cnrs.fr

The manuscript was written through contributions of all authors.

  Figure I: Continuous flow reactor used in this study for the synthesis of ZrO2 nanoparticles. Nitrate or alkoxide zirconium precursors are dissolved in water or ethanol, respectively, in the precursor solution.

FEFF

  © program was used to simulate multiple scattering calculations of X-ray Absorption Fine Structure of undoped tetragonal ZrO2 nanocrystals (ICSD file #92091) and used as starting point for the structural refinement. The refined parameters where: the number of oxygen atoms (NO(I) and NO(II)) in the first and second shells; the number of zirconium atoms (NZr) in the third shell; the energy shift (ΔE0); the adjustment of the half path length (RZr-O(I), RZr-O(II) and RZr-Zr); and the relative mean square displacement related to the half path length (σ 2 ). The nanoparticles were ground and dispersed into a boron nitride matrix and pressed into pellets (5 mm in diameter and 2mm thick) prior to measurements.

:

  PXRD patterns of ZrO2 powders recovered after continuous flow synthesis are plotted in Figure II over the 20 < 2θ < 70° range and compared with the expected hkl reflection positions and relative intensities for m-and t-ZrO2 polymorphs (from the ICSD database collection codes 82543 and 92091, respectively). The weight fraction (%) and A.C.S (nm) of m-and t-ZrO2 obtained from Rietveld refinements of ZrO2 powder PXRD data are listed in Table 2. The PXRD reflections for sample ZrO2-01 show a clear visual agreement with expected m-ZrO2 Bragg reflections -as evidenced by intense reflections at 2θ values of ≈ 24.2°, 28.3°, and 31.5°, (011)m, (11-1)m, and (111)m reflections, respectively. However samples ZrO2-02 to ZrO2-05 show a clear indication of drastically increasing t-ZrO2 phase content evidenced by the single (101)t reflection located at 2θ ≈ 33.3°. Bragg reflections for the m-ZrO2 phase are barely visible in the PXRD data from samples ZrO2-05 to ZrO2-08.

  Figure II: X-ray diffraction patterns of ZrO2 powders in the 20 to 70° 2ϴ range, emphasizing the change in structure depending on the amount of water available for the synthesis (ZrO2-01 to 04), the precursor concentration (ZrO2-05 and 06), and the use of surfactants (ZrO2-07 and 08). m-and t-ZrO2 structures are represented and indexed above and below the XRD patterns, respectively.

Figure

  Figure III: FTIR spectra for a) ZrO2-05 powder without surfactant, b) ZrO2-07 with a 1:6 molar ratio of [Zr 4+ ]:[HA] and c) ZrO2-08 containing a 1:12 [Zr 4+ ]:[HA] molar ratio, confirming the surfactant grafting.

  leads to a slight increase in m-ZrO2 content in the final powders. The addition of hexanoic acid slightly reduces both the t-ZrO2 A.C.S. from ≈ 3 nm down to ≈ 2 nm as well as the m-ZrO2 A.C.S. from ≈ 3.5 nm down to ≈ 2.5 nm for a [Zr 4+ ]:[HA] molar ratio equal to 1:6. Further increase in HA does not seem to modify anymore the t-ZrO2 A.C.S. however, as t-ZrO2 A.C.S. for ZrO2-08 is also ≈ 2 nm.Transmission electron microscope (TEM) and high-resolution transmission electron microscope (HRTEM) images of ZrO2 powders are shown in Figure V. Particle sizes were averaged over at least 70 particles for all samples to improve statistics on the mean particle size and standard deviation. ZrO2 nanoparticles are quasi-spherical in shape, and their particle sizes obtained from microscopy observations are listed in Table2alongside A.C.S. values obtained by Rietveld refinements. Besides the case of ZrO2-01 where a significant difference is observed with an A.C.S. of 4.2 nm and an A.P.S of 6.6 nm indicating that the particles are not to fully crystallized (linked to the difference in precursors), a good agreement is observed for the other samples confirming the size observations made by PXRD analysis. The selected area electron diffraction (SAED) images realized over a large number of ZrO2 nanoparticles show a high degree of crystallinity with an increasing quantity of the t-ZrO2 phase from: ZrO2-02 (Figure V j) to ZrO2-07 (Figure V m). Also, A.P.S. observed by TEM, HRTEM and Rietveld refinements are very similar, and it can be seen (Table

Figure

  Figure IV: Raman spectra of ZrO2 powders collected at room temperature in the 110-700 cm -1 domain. Raman spectra were separated for clarity purposes: on the left hand side, those displaying a [Zr 4+ ] = 10 -2 mol/L, (ZrO2-01 to ZrO2-04) and decreasing m-ZrO2 content; while those on the right hand side with stabilized m-ZrO2 content (ZrO2-05 to ZrO2-08). The monoclinic (M) and tetragonal (T) domains are separated by red dashed-lines and t-and m-ZrO2 Raman modes used for the quantification of each phase are represented by * and # symbols, respectively.

  Figure VI illustrates the correlation between structural proportion variations and nanoparticle A.C.S. between each synthesized powder in this study. In the first row of Figure VI (ZrO2-01 to 04

Figure

  Figure V: a, TEM image of ZrO2-01. b, c, d, e, f, g and h are HRTEM images of ZrO2-02, 03, 04, 05, 06, 07 and 08, respectively. i plot the particle size distribution for each sample, determined using a normal distribution function over the particle counts. j, k, l and m are diffraction images of ZrO2-02, 04, 05 and 07, respectively. t-and m-ZrO2 diffracting planes are labelled on the left-and right-hand side of the diffracting images, respectively.

Figure

  Figure VI: Visualization in t-and m-ZrO2 structure proportions variation in cyan and magenta, respectively, for each of the 8 powders synthesized in this study. The size variation of both t-and m-ZrO2 diffracting domain (A.C.S.) are represented in squares for ease of comprehension.

Figure

  Figure VIII. a) Zr K-edge normalized EXAFS spectra of ZrO2-05 powder, and b) the corresponding Fourier transform (black lines) corrected with the phase shift of the first coordination shell; its associated best fit (red line).This phenomenon leads therefore to an overestimation of the NO coordination number. Thus, the number of O nearest neighbors and their associated uncertainties do not allow us to distinguish between the monoclinic and tetragonal structures, displaying 7 and 8 O atoms per unit cell, respectively. Also, the refined Zr-O distances RZr-O(I) and RZr-O(II) were 2.11 ± 0.07 Å and 2.30 ± 0.07 Å, respectively. This is in quite good agreement with the distances estimated from the PDF data where the dZr-O(I) was estimated at 2.07 ± 0.01 Å and the dZr-O(II) 2.38 ± 0.01 Å, keeping in mind that the PDF data includes overlapping atom pair information making it less reliable than X-ray absorption spectroscopy. These values, though close to that of the model, are hardly distinguishable from those of the monoclinic structure, displaying 5 Zr-O distances ranging from 2.03 Å up to 2.26 Å.When looking at the Zr-Zr shell, a refined number of Zr atoms NZr of 9.6 ± 1.4 is obtained, which is slightly lower than 12 expected for a pure tetragonal structure but too high for a monoclinic structure where a value of NZr = 6 is expected. However, the refined Zr-Zr distance RZr-Zr = 3.64 ± 0.09 Å is very close to the single Zr-Zr bond distance of the tetragonal model (3.56 Å) that was used to perform FEFF calculation and fit the experimental data, while for a monoclinic structure two shorter Zr-Zr bond distances are generally observed at 3.34 Å and 3.46 Å. For information, the distances estimated from the PDF data were divided into two contributions the dZr-Zr(I) and dZr-Zr(II) with values of 3.60 ± 0. 01 Å and 3.63 ± 0.01 Å, which is in good agreement with EXAFS. Therefore, the obtained fitting results tend to indicate the existence of an intermediate structure which is not clearly tetragonal, neither monoclinic25,70,71 . In addition, for very small nanoparticles, the proportion of surface atoms over core ones increases dramatically, in our case, for a 2 nm spherical particle, since the Zr-Zr distance is 3.64 Å, there are approximately only five Zr atoms across the particle diameter, meaning two at the surface and three in the core. Knowing that surface Zr atoms are generally not fully coordinated, this also supports our observation of an intermediate structure, especially regarding the local environment of Zr atoms and its coordination to O atoms.

Figure VII :

 VII Figure VII: Differential correlation functions D(r) obtained for powders ZrO2-02, 05 and 06 in the 1.7-50 Å range. Blue dotted lines, red and green curves symbolize the experimental data sets (D(r) functions), the best fit and difference, respectively. The insets are the PDFs for each of the 3 measured samples in the 1.5-10.5 Å range.

Table 1

 1 lists non-exhaustively the pros and cons of different syntheses routes generally used to synthesize ZrO2 nanoparticles.

Table 1 . Pros and cons of different syntheses routes for the formation of ZrO2 nanoparticles regarding the process duration, the existence or not of post synthesis steps and the quality of the produced powders in terms of average particle size (A.P.S.) and homogeneity.

 1

	Processes	Duration	Post synthesis step(s)	A.P.S. (nm)	Homogeneity	Ref
	Dry
	Solid state	Long	Milling, annealing	7-17	Low	28,
	Deposition	Long	Drying, annealing	4-40	Medium	29, 22, 30
	Wet
	Hydrothermal	Moderate	Drying	8-30	High	14, 31
	Sol gel	Long	Drying, annealing	7-500	High	32, 33, 17, 16, 34, 35,
	N.H.S.G.	Long	None	2-4	Very high	36, 24, 37,
	Supercritical fluids	Short	None	5-50	Very high	38, 39
	It appears that both non-hydrolytic sol-gel (N.H.S.G.) reactions
	and the supercritical fluids technology offer the possibility to
	synthesize ultra-fine ZrO2 nanoparticles exhibiting very high

size homogeneity and no post-synthesis step to finalize nanoparticle crystallization. However, though N.H.S.G. reactions allow the formation of almost pure t-ZrO2 nanoparticles with fine control over size and size distribution

Table 2 . Experimental conditions and characterization results for all samples in the present study.

 2

		ZrO2-01	ZrO2-02	ZrO2-03	ZrO2-04	ZrO2-05	ZrO2-06	ZrO2-07	ZrO2-08
	Injection lines	1	2	2	1	1	1	1	1
	Solvent	H2O	H2O / Ethanol	Ethanol / Ethanol	Ethanol	Ethanol	Ethanol	Ethanol	Ethanol
	Water mol. %	100	71	1.9	0.8	< 0.8	< 0.8	< 0.8	< 0.8
	p(MPa)	25	25	25	25	25	25	25	25
	T (°C)	400	400	400	400	400	400	400	400
	Flow rate (mL/min -1 )	8	4 / 3.25	3.25 / 3.25	6.5	6.5	6.5	6.5	6.5
	[Zr 4+ ] (mol/L)	10 -2	10 -2	10 -2	10 -2	3.3 10 -2	6.6 10 -2	3.3 10 -2	3.3 10 -2
	[Zr 4+ ]:[HA]	-	-	-	-	-	-	1:6	1:12
	m-ZrO2 wt. % Raman	100	70 ± 5	40 ± 4	29 ± 4	11 ± 3	12 ± 3	15 ± 3	16 ± 3
	m-ZrO2 wt. % Rietveld	100	60 ± 1	36 ± 1	19 ± 1	17 ± 1	13 ± 1	14 ± 1	17 ± 1
	m-ZrO2 A.C.S. (nm) Rietveld	4.2 ± 0.1	5.2 ± 0.1	5.0 ± 0.1	4.0 ± 0.1	4.0 ± 0.1	3.5 ± 0.1	3.0 ± 0.1	2.6 ± 0.1
	t-ZrO2 A.C.S. (nm) Rietveld	-	4.7 ± 0.1	5.5 ± 0.1	3.9 ± 0.1	3.7 ± 0.1	2.8 ± 0.1	2.4 ± 0.1	2.4 ± 0.1
	A.P.S. (nm) TEM	6.6 ± 1.2	6.0 ± 0.9	5.0 ± 1.0	4.3 ± 0.6	3.7 ± 0.5	3.9 ± 0.6	3.8 ± 0.4	3.2 ± 0.5
	m-ZrO2	a=5.158(1)	a=5.154(1)	a=5.165(1)	a=5.165(1)	a=5.165(1)	a=5.165(1)	a=5.165(1)	a=5.165(1)
	lattice	b=5.201(1)	b=5.204(1)	b=5.193(2)	b=5.193(2)	b=5.193(2)	b=5.193(2)	b=5.193(2)	b=5.193(2)
	parameters (Å)	c=5.313(1)	c=5.322(1)	c=5.344(2)	c=5.344(2)	c=5.344(2)	c=5.344(2)	c=5.344(2)	c=5.344(2)
	t-ZrO2 lattice parameters (Å)	-	a=3.602(1) c=5.174(2)	a=3.601(1) c=5.182(1)	a=3.605(1) c=5.156(2)	a=3.603(1) c=5.134(3)	a=3.621(1) c=5.083(2)	a=3.643(1) c=5.046(2)	a=3.646(1) c=5.034(2)

Refined fits of the Raman spectra are displayed in the supplementary information in Figure

S1

ZrO 2 -01 ZrO 2 -02 ZrO 2 -03 ZrO 2 -04 ZrO 2 -05 ZrO 2 -06 ZrO 2 -07 ZrO 2 -08 i (101) t (110) t /(002) t (112) t /(020) t (103) t /(211) t (004) t /(220) t (301) t /(213) t (100) m (011) m /(110) m (11-1) m (111) m (101) t (110) t /(002) t (112) t /(020) t (103) t /(211) t (004) t /(220) t (301) t /(213) t (101) t (110) t /(002) t (112) t /(020) t (103) t /(211) t (004) t /(220) t (301) t /(213) t (101) t (110) t /(002) t (112) t /(020) t (103) t /(211) t (004) t /(220) t (301) t /(213) t

	a	b	c	d
	j			k
	l			m
	e	f	g	h

Table 3 : Structural characteristics obtained from D(r) re- finements

 3

					the amount of water during the synthesis (sample ZrO2-02 vs.
					samples ZrO2-05 and ZrO2-06) will clearly reduce the amount
					of monoclinic phase. The highest percentage of t-ZrO2 in the
					series of samples was observed for ZrO2-05, based on both
					Rietveld refinements (17 wt. %) and Raman spectroscopy (11
					wt. %). This was also confirmed with diffraction images col-
					lected on ZrO2-05 showing the lowest dispersion around t-ZrO2
					Bragg reflections as shown in Figure VII (l), thus implying
					lower amounts of m-ZrO2 in the powder. EXAFS measurements
					were performed on ZrO2-05 with the objective, by probing the
					first Zr-O and Zr-Zr coordination shells around zirconium, to
					confirm the stabilization of a majority tetragonal-like phase.
					The fits were done for radial distances from R=1.5Å to R=4.5Å
					and for k values from 3.8 to 14.6 (Figure VIII). Considering the
					Zr-O diffuse shell, the starting model present an arrangement of
					8 backscattering O atoms around a central Zr absorbing atom. 4
					O atoms (NO(I)) were distant of 2.00 Å (RZr-O(I)) and the other 4
					(NO(II)) distant of 2.40 Å (RZr-O(II)). The Zr-Zr diffuse shell was
					fitted using a single shell model of 12 Zr atoms (NZr) around a
		ZrO2-02	ZrO2-05	ZrO2-06	central Zr atom, with all 12 Zr atoms distant of 3.56 Å (RZr-Zr).
					Values obtained after the refinement are detailed in Table 4.
	m-ZrO2 wt. %	68.8(2)	26.9(3)	33.6(4)
	t-ZrO2 Cell Vol/Z (Å 3 )	33.6(1)	33.5(1)	33.4(1)
	m-ZrO2 Cell Vol/Z (Å 3 )	35.3(1)	35.4(1)	35.4(1)
	Domain size (nm)	6.3 ± 0.1	5.5 ± 0.1	4.2± 0.1
	RWP (%)	12.4	11.6	10.9
	The D(r) data, refined model and difference curves displayed in
	Figure VII show that the observed ZrO2 nanoparticles synthe-
	sized are highly ordered at a local scale and can be well de-
	scribed by a mixture of m-ZrO2 and t-ZrO2 structural models.
	Information obtained from D(r) refinements show that crystal-
	lite domain sizes are ≈ 6.3, 5.5 and 4.2 nm for ZrO2-02, 05 and
	06, respectively, which is very close to values obtained from
	Rietveld PXRD refinements (5, 3.9 and 3.1 nm, respectively).
	Also, from PDF analysis, m-ZrO2 wt. % obtained from the re-
	finements are ≈ 68, 27 and 34 for ZrO2-02, 05 and 06, respec-
	tively. For ZrO2-02, this is in good agreement with Rietveld re-
	finement (60 wt. %) and Raman quantification (70 wt. %). For
	ZrO2-05 the results start to diverge, with Rietveld refinement
	we obtained 17 wt. % and with Raman quantification 11 wt. %.
	This is even more pronounced for ZrO2-04 where Rietveld re-
	finement gave 19 wt. % and Raman quantification 29 wt. %.
	Nevertheless, we have to keep in mind that, the Bragg reflection
	intensity of the monoclinic phase is much lower than the higher
	symmetry tetragonal phase, and thus, especially for low m-ZrO2
	phase fractions with small domain sizes, it is difficult to obtain
	a precise quantification by lab-based PXRD measurements in
	comparison with the superior signal-to noise and real space res-
	olution of the synchrotron based PDF data. Similarly, the values
	obtained from the Raman quantification based on a lab scale
	apparatus can only give trends. On top of this there is still this
	crystal phase distortion at the surface of the particle resulting
	from the strain generated at this scale, and differences in probe
	sample depth sensitivity which should be considered. In conclu-
	sion, considering differences in the sensitivity and length scales
	probed (e.g. local vs long range ordering), we can conclude that
	the results are consistent, especially regarding the high degree
	of crystallinity of the particles since sizes from PXRD and PDF
	are similar. For the phase ratios, we can clearly see that reducing

Table 4 . List of the EXAFS fitting results for ZrO2-05. For the interatomic distance refinements, the first subscript re- fers to the absorbing element (Zr) while the second sub- script refers to the backscattering element which can be ei- ther Zr, O(I) or O(II) atoms.

 4

	Independent points	30
	Number of variables	22
	ΔE0 (eV)	0.0026 ± 1.1625
	NO(I)	5.9 ± 1.1
	NO(II)	2.7 ± 0.6
	NZr	9.6 ± 1.4
	R Zr-O(I) (Å)	2.11 ± 0.07
	R Zr-O(II) (Å)	2.30 ± 0.07
	R Zr-Zr (Å)	3.64 ± 0.09
	σ 2 O (Å 2 )	0.0050 ± 0.0014
	σ 2 Zr (Å 2 )	0.0086 ± 0.0009

The Fourier transform clearly shows well resolved peaks at ≈ 2 and 3.7 Å attributable to Zr-O and Zr-Zr nearest neighbor bond distances, respectively. The refined number of O atoms in the first and second coordination shells (NO(I) + NO(II)) is ≈ 8.6. Taking into account an uncertainty larger than 1, this result is consistent with the expected value of 8 for a tetragonal structure, and can be considered as reasonable. Moreover, the decrease in surface metal-metal coordination number comparatively to core metal-metal coordination number can be explained by the confinement of the crystallite surface creating different local environments between surface and core metal atoms

69

All authors have given approval to the final version of the manuscript.

The work presented is carried out with funding from the Innovation Fund Denmark (GCAM) for A. Auxéméry.

The authors declare no competing financial interest.

The authors acknowledge the support of the Innovation Fund Denmark (GCAM), the Centre National de la Recherche Scientifique (CNRS) and the Région Nouvelle Aquitaine. The authors also acknowledge the European Synchrotron Radiation Facility for provision of synchrotron radiation beamtime and facilities, the FAME team for assistance in using their beamline and the Diamond Light Source for mail-in PDF measurement access at Beamline I15-1 under Proposal CY22774.


Jerome Molénat

Cécile Dagés

Juliette Burtin

Maroua Bouteffeha

Insaf Mekki

Can small reservoirs be useful to gauge stream runoff ?

Measurement of stream runoff in remote regions, especially in Southern countries, is still a major challenge. In arid and semi-arid regions, small reservoirs have been built for decades. The initial roles of these reservoirs can be manifold: water storage for agricultural use, flood storage area, sediment retention,. . . These reservoirs can also be used as runoff gauges by remotely measurement of their surface areas and water height (Liebe et al., 2005). The principle is to estimate the stream runoff from reservoir water balance considering that all terms (evaporation, percolation, rainfall, pumping for irrigation or other human use, reservoir water volume) are known or measured. The stream runoff is then estimated as the default term in the mass balance. Different sources of error affect then the stream runoff estimation: errors in evaporation and rainfall rate measurements, in reservoir water height measurement, in percolation estimation. . . So far, the issue of uncertainty has been not heavily examined.

In this work, we analyze the interests and shortcomings of small reservoirs as stream runoff gauges. More specifically, we aim i) at comparing stream runoff derived from reservoir water monitoring with measurements made more classicaly with a weir gauging station, ii) at quantifying the total uncertainty in stream runoff estimation derived from reservoir water monitoring and ii) at analyzing and quantifying the weight of the different sources of error in the total uncertainty in the estimation. The sensitivities of the different sources of errors are quantified for annual, monthly, and daily time periods with the intent of identifying the key sources impacting the stream runoff estimation. The study is based on the Kamech catchment (Tunisia), catchment belonging to OMERE observatory and to OZCAR, the French network of Critical Zone Observatories.