Shinji Ozaki

Clémentine Fritsch

Benoît Valot

Frédéric Mora

Thierry Cornier

Renaud Scheifler

Francis Raoul

Benoit Valot

How Do Richness and Composition of Diet Shape Trace Metal Exposure in a Free-Living Generalist Rodent, Apodemus sylvaticus

Keywords: Food identification, Metabarcoding, Trophic transfer, Wildlife, Salicaceae

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Trace metals (TMs) are naturally occurring substances in trace amounts, in a proportion below 0.1%, in natural media. [START_REF] Madrid | Heavy Metals": Reminding a Long-Standing and Sometimes Forgotten Controversy[END_REF] Although some of them are essential for life, TMs can pose risks to both wildlife and human health at elevated concentrations resulting from anthropogenic activities. [START_REF] Walker | Principles of Ecotoxicology[END_REF] The assessment and the mitigation of environmental risk of TM contamination of ecosystems require both a sound understanding and an accurate estimate of the exposure of free-living organisms to TMs. The exposure of wild mammals to TMs occurs mainly through consumption of contaminated food. [START_REF] Shore | Ecotoxicology and Environmental Toxicology Series[END_REF] The composition of diet has been focused on as an important aspect of the variation of exposure of small mammals to TMs. For instance, earthworms or snails, organisms that may accumulate TMs at high concentrations in their tissues, have been considered to be important contributors for exposure to TMs in mammals consuming them. [START_REF] Hamers | Risk Assessment of Metals and Organic Pollutants for Herbivorous and Carnivorous Small Mammal Food Chains in a Polluted Floodplain (Biesbosch, The Netherlands)[END_REF][START_REF] Rogival | Transfer and Accumulation of Metals in a Soil-dietwood Mouse Food Chain along a Metal Pollution Gradient[END_REF][START_REF] Van Den Brink | Effects of Soil Properties on Food Web Accumulation of Heavy Metals to the Wood Mouse (Apodemus Sylvaticus)[END_REF] However, mammals can consume simultaneously numerous different items. Although ingesting some metal accumulator items leads to an increase in exposure, many other less TM accumulator items could reduce total TM concentrations in the diet. A wide variety of items consumed may result in two opposite effects: increased opportunity of consuming TM accumulators or reduced TM concentrations of ingested food. However, few studies have assessed the accurate diet composition and richness related to the TM contamination in mammals, [START_REF] Orłowski | Relationships between Stomach Content and Concentrations of Essential and Non-Essential Elements in Tissues of Omnivorous Nestling Rooks Corvus Frugilegus: Is the Size and Composition of Stomach Content Relevant[END_REF] and, to our knowledge, no study have investigated the relationships between the oral exposure of mammals to TMs and such diet richness.

The accurate determination of items actually consumed is a major challenge in wildlife diet assessment because identification at high taxonomic resolution is almost impossible with classical macro-or microscopy-based methods. However, current technological development such as high-throughput next generation sequencing has led a rise of new DNA-based techniques as a powerful approach for multispecies identification using degraded DNA extracted from wide range of environmental samples, i.e. soil, water or feces (eDNA metabarcoding). [START_REF] Valentini | DNA Barcoding for Ecologists[END_REF][START_REF] Pompanon | Who Is Eating What: Diet Assessment Using next Generation Sequencing[END_REF][10][START_REF] Taberlet | Towards next-Generation Biodiversity Assessment Using DNA Metabarcoding[END_REF][START_REF] Bohmann | Environmental DNA for Wildlife Biology and Biodiversity Monitoring[END_REF] The efficiency of the eDNA metabarcoding has been demonstrated in diet analysis in various mammals. [START_REF] Soininen | Analysing Diet of Small Herbivores: The Efficiency of DNA Barcoding Coupled with High-Throughput Pyrosequencing for Deciphering the Composition of Complex Plant Mixtures[END_REF][START_REF] Bohmann | Molecular Diet Analysis of Two African Free-Tailed Bats (Molossidae) Using High Throughput Sequencing[END_REF][START_REF] Ait Baamrane | Assessment of the Food Habits of the Moroccan Dorcas Gazelle in M'Sabih Talaa, West Central Morocco, Using the trnL Approach[END_REF][START_REF] De Barba | DNA Metabarcoding Multiplexing and Validation of Data Accuracy for Diet Assessment: Application to Omnivorous Diet[END_REF][START_REF] Soininen | Arctic Small Rodents Have Diverse Diets and Flexible Food Selection[END_REF] This method is expected to identify in detail the diet composed of numerous items in generalist small mammals like the wood mouse (Apodemus sylvaticus), [START_REF] Watts | The Foods Eaten by Wood Mice (Apodemus Sylvaticus) and Bank Voles (Clethrionomys Glareolus) in Wytham Woods, Berkshire[END_REF][START_REF] Butet | Régime Alimentaire D'une Population de Mulots Sylvestres (Apodemus Sylvaticus L., 1758), Dans Une Lande Xéro-Mésophile En Cours de Recolonisation Végétale[END_REF][START_REF] Rogers | The Diet of the Wood Mouse Apodemus Sylvaticus on Setaside Land[END_REF][START_REF] Abt | Seasonal Variations of Diet Composition in Farmland Field Mice Apodemus Spp. and Bank Voles Clethrionomys Glareolus[END_REF] a rodent widely spread in Europe [START_REF] Quéré | Les rongeurs de France : Faunistique et biologie[END_REF] and frequently used for monitoring of environmental metal pollution, [START_REF] Rogival | Transfer and Accumulation of Metals in a Soil-dietwood Mouse Food Chain along a Metal Pollution Gradient[END_REF][START_REF] Van Den Brink | Effects of Soil Properties on Food Web Accumulation of Heavy Metals to the Wood Mouse (Apodemus Sylvaticus)[END_REF][START_REF] González | Considerations on the Sample Size of Wood Mice Used to Biomonitor Metals[END_REF][START_REF] Sánchez-Chardi | Bioaccumulation of Metals and Effects of a Landfill in Small Mammals. Part II. The Wood Mouse, Apodemus Sylvaticus[END_REF][START_REF] Tête | Histopathology Related to Cadmium and Lead Bioaccumulation in Chronically Exposed Wood Mice, Apodemus Sylvaticus, around a Former Smelter[END_REF] and then to allow associating the oral exposure of the rodent to TMs and both the composition and the richness of their diet.

The present study analyzed the relationship between exposure to TMs of wood mice captured along a gradient of soil TM contaminations in a smelter-impacted area and their diet determined by metabarcoding from their stomach content. We hypothesized that (1) the consumption of (hyper-)accumulator items like earthworms induces a high exposure of small mammals to TMs and that (2) such exposure is shaped by the diet richness due to a "dilution" effect where high exposure related to accumulator items is reduced by the consumption of less accumulator items in case of high diet richness.

Materials and methods

Study Area. The study area was located in Northern France (Noyelles Godault, Hauts-de-France, France). Due to metal containing dust released from the former lead (Pb) and zinc (Zn) smelter "Metaleurop Nord", the soils surrounding the smelter are highly contaminated by TMs, especially cadmium (Cd), Pb and Zn. [START_REF] Sterckeman | Assessment of the Contamination of Cultivated Soils by Eighteen Trace Elements around Smelters in the North of France[END_REF][START_REF] Douay | Contamination of Urban Soils in an Area of Northern France Polluted by Dust Emissions of Two Smelters[END_REF][START_REF] Douay | Contamination of Woody Habitat Soils around a Former Lead Smelter in the North of France[END_REF][START_REF] Waterlot | Prediction of Extractable Cd, Pb and Zn in Contaminated Woody Habitat Soils Using a Change Point Detection Method[END_REF] Total TM concentrations in soils of woody habitats in an area of 40km 2 around Metaleurop Nord range from 0.1 to 237 mg kg -1 of dry soil for Cd, from 16 to 7331 mg kg -1 for Pb, and from 44 to 7264 mg kg -1 for Zn, which dramatically exceed the concentrations in a reference site (0.9-2, 43-200 and 89-278 mg kg -1 of dry soil for Cd, Pb, and Zn, respectively) from the same region. [START_REF] Fritsch | Spatial Distribution of Metals in Smelter-Impacted Soils of Woody Habitats: Influence of Landscape and Soil Properties, and Risk for Wildlife[END_REF] The present study was undertaken on seven sites of 500m x 500m, located along a soil contamination gradient and composed of three types of dominant habitats (for details see Supporting Information Table S1). TM concentrations in soils of woody habitats, expressed as mg kg -1 of dry soil, were based on data published in Fritsch et al. [START_REF] Fritsch | Spatial Distribution of Metals in Smelter-Impacted Soils of Woody Habitats: Influence of Landscape and Soil Properties, and Risk for Wildlife[END_REF] and used for statistical analyses. Sample Collection. Rodent Trapping. Wood mice were captured in spring (April) and in autumn (September and October) 2012, in accordance with current French legislation about ethics and use of animals in research. In each season and each site, 10 trap lines composed of 10 small break-back traps (3 m spaced each) were used with peanut as rodent's bait following Fritsch et al. [START_REF] Fritsch | Spatially Explicit Analysis of Metal Transfer to Biota: Influence of Soil Contamination and Landscape[END_REF] All trap lines were set in woody habitats and their position was geo-referenced.

The trap lines were checked in the morning for 3 consecutive days and re-set and/or re-baited, if necessary. Captured animals were immediately frozen and stored at -20ºC for further analyses in the laboratory. For this study, 200 wood mice composed of 95 females, 103 males and two individuals whose sex could not be identified were used. The sex-ratio was not significantly different from 1:1 (chi-squared = 0.32, p-value = 0.57). One hundred and three mice were taken in spring and 97 in autumn. The ratio between the two seasons was not significantly different from 1:1 (chi-squared = 0.18, p-value = 0.67).

Stomach Content Extraction.

Stomach content (SC) was extracted with a spatula from each rodent's body thawed at room temperature. After removing remaining bait, each SC was homogenized and split into two aliquots. One aliquot (about 10 mg) was stored in 95 % ethanol for the metabarcoding analysis. The other was used for measuring TM concentrations which were used to estimate exposure of mice to metals via the food ingested within the last few hours.

The spatula was thoroughly cleaned with disposable tissues, then washed with ultra-pure water (18.2 MΩ/cm 2 by Millipore Milli-Q Integral 3) and wiped off with other tissue between each extraction.

Metal Analysis. The SCs for TM concentration analysis were dried at 50°C in an oven until obtaining constant mass. The dried samples were digested in HNO3 (67-69 %; Fisher Scientific Bioblock, ultratrace quality Optima) with H2O2 (Fisher Scientific Bioblock) by using Digiprep (SCP Sciences). The samples were then diluted by adding ultra-pure water (18.2 MΩ/cm 2 ). TM concentrations were measured with an inductively coupled plasma mass spectrometry (ICP-MS: X Series II, ThermoFischer Scientific) and expressed as μg g -1 of dry mass (DM). Analysis accuracy was checked by using certified reference materials (INCT-OBTL-5: Oriental Basma Tobacco Leaves). Mean and standard deviation of recovery rate for Cd, Pb and Zn were respectively 107.2 ± 4.7, 83.6 ± 9.6 and 100.7 ± 9.1 %.

Diet Assessment Using Metabarcoding Analysis. DNA Extraction, Amplification and

Sequencing. The DNA extraction, amplification and the PCR purification were performed at SPYGEN facilities (www.spygen.com). DNeasy Blood and Tissue Kit (Qiagen GmbH) was used for extracting total DNA, following the manufacturer's instruction. DNA amplification was performed with three sets of primers: primer targeting the P6 loop of the chloroplast trnL (UAA) intron g/h 32 was used for identifying general plant species, primer targeting a short fragment of mitochondrial 16S gene (16S mtDNA) was for arthropod and mollusk DNA (16SMAV-F/16SMAV-R) [START_REF] De Barba | DNA Metabarcoding Multiplexing and Validation of Data Accuracy for Diet Assessment: Application to Omnivorous Diet[END_REF] , and primer targeting a short region of 16S mtDNA for earthworm (ewD/ewE) [START_REF] Bienert | Tracking Earthworm Communities from Soil DNA[END_REF] (for details of those primers, see Supporting Information Table S2). PCR amplifications were carried out on Applied Biosystems Veriti 96 Wells (Life Technologies). The amplification was realized in a final volume of 25 μL using 3 μL of DNA extract. Two PCR replicates were performed per each sample. For the amplification of arthropod and mollusc DNA, 2 µM of a blocking primer for mammal's DNA (MamMAVB1 16 ) were added in the PCR mix. The amplification mixture contained 1 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems), 10 mM of Tris-HCl, 50 mM of KCl, 2.5 mM of MgCl2, 0.2 mM of each dNTP, 0.2 μM of groupspecific primers, 0.2 μg/μL of bovine serum albumin (BSA, Roche Diagnostic) and ultra-pure water (18.2 MΩ/cm2) to bring each sample to the final volume. The mixture was denatured at 95°C for 10 min, followed by 45 cycles of 30 s at 95°C, 30 s at 50°C for trnL-g/h, at 55°C for 16SMAV-F/16SMAV-R and at 58°C for ewD/ewE and 1 min at 72°C, followed by a final elongation at 72°C for 7 min. Extraction and PCR negative controls were analyzed in parallel in order to monitor potential contamination. After amplification, the samples were purified using a MinElute PCR purification kit (Qiagen GmbH). Before sequencing, purified DNA was titrated using capillary electrophoresis (QIAxcel; Qiagen GmbH). The purified PCR products were pooled in equal volumes, to achieve an expected sequencing depth of 10,000 reads per sample.

Libraries were prepared using TruSeq Nano DNA genomic kit (Illumina) and a pair-end sequencing (2x100 bp) was carried out with an Illumina HiSeq sequencer (Illumina) using TruSeq SBS Kit v3 (Illumina) following the manufacturer's instructions. Library preparation and sequencing were performed at Fasteris facilities (www.fasteris.com).

Bioinformatics. Reads were handled by the software Mothur pipeline. [START_REF] Schloss | Introducing Mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities[END_REF] Forward and reverse reads were assembled in contig sequences. Sequences were filtered based on length (20-90bp for trnL-g/h; 36-38bp for 16SMAV; 69-81bp for ewD/ewE), homo-polymer (less than 10 nucleotides) and no ambiguous nucleotides. After de-replication (count occurrence of each different sequence), only unique sequences with a minimum count of 10 (sum of all samples) were used for further analyses. The data was applied both to diet richness estimation and to identification of food composition.

Diet Richness Estimation. Molecular Operational Taxonomic Units (MOTUs) were used for measuring diet richness. Sequences were clustered for each primer with average neighbor algorithm using Needlman-Wunsch distance. The cutoff values were chosen as effective clustering on the basis of relationships between number of clusters and distance: 0.032, 0.042, and 0.034 for sequences obtained from the primers trnL-g/h, 16SMAV and ewD/ewE, respectively. After using an occurrence of 100 as a threshold to remove background sequencing data, the sequence data were converted into presence/absence of MOTUs because the number of sequences from degraded DNA is likely not proportional to the biomass of each taxa really eaten. [START_REF] Deagle | Quantifying Sequence Proportions in a DNA-Based Diet Study Using Ion Torrent Amplicon Sequencing: Which Counts Count?[END_REF] Food Composition. For the detail of food identification method, see Ozaki et al. [START_REF] Ozaki | Does Pollution Influence Small Mammal Diet in the Field? A Metabarcoding Approach in a Generalist Consumer[END_REF] Briefly, each sequence extracted from SCs (i.e., query sequence) was compared with reference sequences of species recorded in the study area, [START_REF] Ozaki | Does Pollution Influence Small Mammal Diet in the Field? A Metabarcoding Approach in a Generalist Consumer[END_REF] taken from the GenBank sequence database (www.ncbi.nlm.nih.gov/genbank). When query sequence matched with more than one reference sequence, query sequences were gathered into one group composed of the corresponding species, and then gathered at the family level. The sequences were finally converted into presence/absence of each plant family in SCs. Invertebrate resources in the field were identified mainly at family level, which could not allow to identify invertebrate food. We used only the presence/absence of arthropod and mollusk items (referred to as only "arthropod" hereinafter) and earthworm items in SCs as food composition.

Statistical Analysis. Statistical analyses were carried out on the 200 mice. As TM concentrations in SCs could be influenced both by soil TM contamination levels through food and ingestion of soils, [START_REF] Beyer | Estimates of Soil Ingestion by Wildlife[END_REF][START_REF] Smith | The Intake of Lead and Associated Metals by Sheep Grazing Mining-Contaminated Floodplain Pastures in Mid-Wales, UK: I. Soil Ingestion, Soil-metal Partitioning and Potential Availability to Pasture Herbage and Livestock[END_REF] we aimed to reduce potential bias related to the level of environmental contamination when quantifying oral exposure as follows. For each TM, a linear positive relationship was observed between the concentrations in SCs and in soils of the woody habitat where each mouse was captured (logarithmically transformed concentrations, p-values of ANOVA < 0.05; for details see Supporting Information Figure S1). The residuals of these linear models were referred to as "exposure" hereinafter and used in following statistical analyses.

Exposure to TMs and items consumed. The exposure was compared between the two seasons, and presence and absence of each identified item in SCs by the non-parametric Wilcoxon-Mann-Whitney test. Among the identified items consumed by wood mice (for details see Ozaki et al. [START_REF] Ozaki | Does Pollution Influence Small Mammal Diet in the Field? A Metabarcoding Approach in a Generalist Consumer[END_REF] ), only items whose occurrence was higher than 10 in the diet of the 200 mice were used in this comparison between presence/absence of items to ensure statistical power of tests: arthropods, earthworms, Betulaceae (e.g. genura Alnus, Betula), Fagaceae (Fagus), Oleaceae (Fraxinus), Poaceae (Arrhenatherum, Holcus Lolium, etc), Rosaceae (Crataegus), Sapindaceae (Acer), Salicaceae (Populus, Salix), and the sequence group composed of Adoxaceae (Sambucus), Asteraceae (Hedera) and Cornaceae (Cornus) items (referred to as "group AAC" hereinafter).

When exposure was significantly related to several variables tested, we used conditional inference trees (CIT [START_REF] Hothorn | Unbiased Recursive Partitioning: A Conditional Inference Framework[END_REF] ) for ranking their importance for the exposure to TMs. This is a recursive binary partitioning analysis that studies significant univariate splits over all possible splitting variables. The splitting variable the most significantly associated to response value will be chosen. Those steps are recursively performed to the two split data, until no significant difference are observed. Permutation tests by Strasser and Weber 40 are applied for the splitting tests. In this study, CIT was performed across binary partitioning variables: season (spring or autumn) and the presence/absence of the 10 types of items mentioned above. Sites were considered to be random effects for exposure in the Wilcoxon-Mann-Whitney test and CIT because of potential effects linked to each site. Those tests were executed for the residuals of the random effect model, i.e. the model with only site as random effect. As all items were used as partitioning variables without taking into account the result of each Wilcoxon-Mann-Whitney test, p-value of CIT was not adjusted by Bonferroni correction.

If CIT suggested an influence of TM of hyper-accumulator items, we then checked whether exposure was explained by the importance of the given items in the diet. In general, ascertaining abundance information using metabarcoding of environmental DNA (eDNA) still lacks consistent evidence due to multiple factors distorting the eDNA-biomass relationship, such as difference in origin (species, organ, etc.) and stability of eDNA and primer bias in amplification. [START_REF] Deiner | Environmental DNA Metabarcoding: Transforming How We Survey Animal and Plant Communities[END_REF] This is particularly true when comparison of different taxa, identified using different primers, is an objective. We calculated the proportion of DNA reads of hyperaccumulator items suggested by CIT in the SC over the total number of DNA reads. In doing so, we did not expect strong bias in comparison because (i) each hyper-accumulator was identified using a single primer and (ii) we assumed that its DNA stability would remain comparable among samples. We therefore assumed that changes in the biomass of a hyper-accumulator ingested would be reflected by changes in the proportion of its DNA sequences over the total number of DNA sequences.

Relationships between exposure and the proportion of sequences of the given hyperaccumulator items within SCs containing the given items was analyzed using a linear mixed model with the site as random effect. The variations explained by the fixed and random effects were estimated by marginal R 2 (R 2 m) and conditional R 2 (R 2 c), respectively. [START_REF] Nakagawa | A General and Simple Method for Obtaining R 2 from Generalized Linear Mixed-Effects Models[END_REF] Relationships between the proportion of the given items and diet richness was also checked using a linear mixed model.

Diet of wood mice. Co-occurrence of items, which could mislead into a fault interpretation in the relationships between exposure and a given item consumed, was checked by CIT: Frequency of occurrence of each plant item was assessed across season and the presence/absence of the other plant items in SCs. In addition, relationship between occurrence of items and TM concentration in soils was analyzed by generalized linear models with logit function for binary data. Probability for occurrence of a given item was compared to a gradient of each TM concentrations in soils. Significance of each model was checked by the likelihood-ratio test compared to the null model. [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R; Statistics for biology and health[END_REF] Meanwhile, diet richness across season was checked by the Wilcoxon-Mann-Whitney test. Each correlation between TM in soils and diet richness was analyzed by the non-parametric Spearman correlation test.

Exposure to TMs and diet richness. Number of plant, arthropod, and earthworm MOTUs in SC were defined as three independent diet richness, and the sum of the three richness as total diet richness. A linear mixed model with the site as random effect was built and analyzed for the exposure to each TM with respect to the total diet richness and to the three richness. The relationships between the exposure, the diet richness, and the presence of specific items were analyzed by model-based recursive partitioning (MOB [START_REF] Zeileis | Model-Based Recursive Partitioning[END_REF] ). The principle of MOB is the same as CIT, i.e. recursive binary partitioning of data by statistical approach, but each terminal node is associated to a model rather than to a variable like CIT. After establishing a model and deciding partitioning variables to involve, MOB assesses whether the model parameters are stable with respect to partitioning variables. If there is a significant instability, [START_REF] Zeileis | A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals[END_REF][START_REF] Zeileis | Generalized M-Fluctuation Tests for Parameter Instability[END_REF] the analysis selects the partitioning variable associated with the highest instability and computes the split point (for quantitative variables) or split class (for qualitative variables) which optimizes the parameter stability. Those steps are repeated in the two split data, until instability is not significant. Finally, significance of explanatory variables in each terminal node was assessed by a type III ANOVA (p-valuenode). In our case, MOB was executed on the basis of mixed linear models. [START_REF] Fokkema | Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees[END_REF] Partitioning variables were the same as for CIT, and p-value of partitioning test was not adjusted. The variations explained by the fixed and random effects of the whole model were estimated by R 2 m and R 2 c, respectively. [START_REF] Nakagawa | A General and Simple Method for Obtaining R 2 from Generalized Linear Mixed-Effects Models[END_REF] Explanatory value of the fixed effects in a terminal node (R 2 node) was also calculated.

All statistical analyses were computed using the statistical software R (ver. 3.4.2; R Development Core Team). The mixed linear models were carried out based on "lme" function in "nlme" package. CIT was performed by "ctree" function in "partykit" package, and MOB based on linear mixed models was performed by "lmertree" function in "glmertree" package.

Results

Diet of mice. Occurrence of items. The frequency of occurrence of plant, arthropod and earthworm items in stomach content of mice is 96.5, 72.5, and 27.0 %, respectively. No significant difference between the two seasons was observed. Frequencies of occurrence of Fagaceae, Oleaceae, Salicaceae, and Sapindaceae items were significantly higher in spring than in autumn (p-value < 0.001 for each). Conversely, Poaceae and AAC group items were more frequent in autumn (p-value < 0.001 for each). The details of the results are provided in Supporting Information Table S3.

The frequency of occurrence of each item was also dependent on the presence of other items.

In spring, the frequency of occurrence of Salicaceae items in SCs was significantly and negatively related to the presence of Sapindaceae items (Supporting Information Figure S2a; pvalue < 0.001). The frequency of occurrence of Salicaceae items was also significantly and negatively related to the presence of the group AAC items when Sapindaceae items were absent in spring (p-value < 0.024). This trend was similar for the frequency of occurrence of Sapindaceae items: in spring, the frequency of occurrence of Sapindaceae items was significantly lower when Salicaceae items were present in SCs, followed by the presence of the group AAC items only when Salicaceae items were absent (Supporting Information Figure S2b; p-value < 0.001 and = 0.019, respectively). The frequency of occurrence of Fagaceae items in autumn was significantly higher when Betulaceae items were present in SCs (Supporting Information Figure S2c; p-value < 0.001). Frequency of occurrence of Oleaceae items was significantly higher when Sapindaceae items were present in SCs (Supporting Information Figure S2d; p-value < 0.01).

When Sapindaceae items were absent, the frequency of occurrence of Oleaceae items was higher when Fagaceae items were present in SCs (p-value = 0.01).

Along the gradient of soil TM concentration, the occurrence of both arthropod and earthworm items did not significantly differ (data not shown). The occurrence of both Salicaceae and Fagaceae items significantly reduced along the gradient of soil TM concentrations only in spring (Supporting Information Figure S3a andS3b; p-value < 0.05 for each of the three TMs in soils), whereas the occurrence of Sapindaceae items significantly increased along the gradient (Supporting Information Figure S3c andS3d; p-value < 0.05) in both spring and autumn. The occurrence of the group AAC items significantly increased along the gradient only in autumn (Supporting Information Figure S3e; p-value < 0.05).

Diet richness. Plant, arthropod and earthworm diet richness ranged from 0 to 6 MOTUs (median: 2), from 0 to 9 (median: 2), and from 0 to 3 (median: 0), respectively. Total diet richness, as well as both plant and arthropod diet richness, were significantly higher in autumn than in spring (data not shown, W = 3463, p-value < 0.001; W = 3646, p-value < 0.001; W = 4190.5, p-value = 0.04, respectively). No significant correlation between the three diet richness values was observed, either in spring or in autumn (Supporting Information Figure S4a andS4b).

Only plant diet richness in spring significantly decreased along the gradient of TMs in soils (Spearman's ρ from -0.26 to -0.19, p-value < 0.05 for the three TMs; Figure S4a).

Exposure to TMs and items consumed. Cd, Pb and Zn concentrations in SCs ranged from 0.03 to 47.1 (median: 0.93), from 0.03 to 194.2 (median: 3.70) and from 5.3 to 337.5 (median: 71.1) µg g -1 DM, respectively. The TM concentration in SCs and the exposure were significantly higher in spring than in autumn for the three TMs (p-value < 0.05; for details see Supporting Information Figure S5). The exposure to Cd was significantly higher when Salicaceae items were present in SCs and significantly lower when AAC plants were present in SCs (W = 2559 and 5986; p-value < 0.05, respectively; Supporting Information Figure S6a). The exposure to Zn was significantly higher when Salicaceae items or Oleaceae items were present in SCs (W = 3073 and 524; p-value < 0.05, respectively; Figure S6b). The exposure to Pb was not influenced by any item consumed.

CIT analysis revealed that season was the most important variable influencing the exposure of rodents to Cd (p-value < 0.001), followed by the presence of Salicaceae items in SC in spring (Figure 1a; p-value = 0.01). The exposure to Cd was the highest when Salicaceae items were present in SC in spring, followed by when Salicaceae item were absent in spring, and the lowest in autumn. For Zn, season was the most important variable (p-value < 0.01), and the presence of Salicaceae items in SC determined the exposure to Zn only in autumn (Figure 1b; p-value < 0.05). The exposure to Zn was the highest in spring, followed by when Salicaceae items were present in SCs in autumn, and the lowest when Salicaceae item were absent in SCs in autumn.

No significant partitioning variable was observed for exposure to Pb.

Only exposure to Cd was positively explained by the proportion of sequence number of Salicaceae items (Figure 2a; p-value < 0.05). R 2 m and R 2 c were 0.079 and 0.251, respectively.

The proportion of sequence number of Salicaceae items was negatively correlated with diet richness of plant (Figure 2b; p-value < 0.01; R 2 m = 0.154; R 2 c = 0.280).

Exposure to TMs and diet richness. Exposure to Cd was significantly and negatively correlated, although weakly, only with the diet richness of earthworms (p-value = 0.04) but not with other richness values (R 2 m = 0.021 and R 2 c = 0.103; data not shown). The relationship between exposure to Cd and the total diet richness was the most significantly conditioned by the presence/absence of Salicaceae items in SCs (the first oval in the upper part of Figure 3a; p-value < 0.001). When Salicaceae items were present in SCs, the exposure was negatively correlated with the diet richness (the scatter plot in the lower part of Figure 3a linked to "Salicaceae" by the line with "yes"; p-valuenode < 0.01, R 2 node = 0.184). Season also significantly conditioned the relationship only for SCs in which Salicaceae items were absent (p-value < 0.01). In spring, the presence of earthworm items significantly conditioned the relationship for SCs in which Salicaceae items were absent (p-value = 0.03). In autumn, the presence of Poaceae items significantly conditioned the relationship for SCs in which Salicaceae items were absent (p-value = 0.03). When both Salicaceae and Poaceae items were absent in SCs in autumn, the exposure was positively correlated with the diet richness (p-valuenode = 0.04, R 2 node = 0.110). The other cases showed no significant relationship between the exposure and the total richness. The relationship between exposure to Cd and the three diet richness values was the most significantly conditioned by presence/absence of Salicaceae items in SCs (Figure 3b; p-value < 0.001). The exposure was negatively correlated with diet richness of plant when Salicaceae items were present in SCs (p-valuenode < 0.001, R 2 node = 0.220). Season significantly conditioned the relationship when Salicaceae items were absent in SCs (p-value = 0.03) but the exposure showed no significant relationship with any diet richness. R 2 m and R 2 c of the models for the total richness were 0.196 and 0.275 and for the three richness were 0.191 and 0.276, respectively.

No significant relationship was observed between the exposure to Zn and both the total and the three richness. However, the relationship between the exposure to Zn and the total diet richness was significantly conditioned by season (Figure 3c; p-value < 0.01). The exposure was positively correlated with the total diet richness in autumn (p-valuenode = 0.04, R 2 node = 0.106). The relationship between the exposure to Zn and the three diet richness was significantly conditioned by season (p-value < 0.01), followed by the presence of Betulaceae items in SC in autumn (Figure 3d; p-value = 0.02). The exposure to Zn was significantly and negatively correlated with the diet richness of earthworm when Betulaceae items were present in SCs in spring (p-valuenode = 0.01, R 2 node = 0.687; it is noteworthy that only two SCs among 12 ones contained 2 different earthworm items). R 2 m and R 2 c of the models for the total richness were 0.060 and 0.187 and for the three richness were 0.098 and 0.196, respectively. No significant relationship was observed between the exposure to Pb and both the total and the three richness. There was no significant conditioning factor for exposure to Pb.

Discussion

Our study shed light on the role of diet richness and composition of food on the exposure to TMs in a generalist species. Our results revealed that high diet richness reduces exposure to TMs, particularly in case of consumption of at least one hyper-accumulator taxa, which however depends on both the metal considered and type of items consumed.

Consuming plant items belonging to Salicaceae family significantly raised the trophic exposure of wood mice to both Cd and Zn. Although precaution should be taken in interpreting relationships between number of DNA sequences and biomass consumed (e.g. Deiner et al. [START_REF] Deiner | Environmental DNA Metabarcoding: Transforming How We Survey Animal and Plant Communities[END_REF] ), a high proportion of Salicaceae family plants in food, which may be considered as a proxy of greater biomass of Salicaceae family plants in the SC, also increased exposure to Cd. Our findings indicated that consuming Salicaceae plants items increased trophic exposure of mice to Cd. Salicaceae family actually includes taxa accumulating Cd to high concentrations in their tissues like leaves (e.g. genera Salix and Populus), [START_REF] Pulford | Phytoremediation of Heavy Metal-Contaminated Land by Trees-a Review[END_REF] and their importance on the exposure of mammals to Cd has been suggested. For instance, Nolet et al. [START_REF] Nolet | Cadmium in Beavers Translocated from the Elbe River to the Rhine/Meuse Estuary, and the Possible Effect on Population Growth Rate[END_REF] showed positive correlations between Cd concentrations in tissues (hairs and kidneys) of beavers Castor fiber and in their main food item, bark of genera Salix and Populus. These genera also accumulate Zn. Brekken and Steinnes 50 argued that genera Salix and Populus could be the most important Cd and Zn contributors for moose Alces alces in a metal-contaminated area. Migeon et al. [START_REF] Migeon | Metal Accumulation by Woody Species on Contaminated Sites in the North of France[END_REF] reported higher concentrations of Cd and Zn in both leaves and stems of genera Salix and Populus than in the other woody species around Metaleurop Nord. The authors also demonstrated their high bioconcentration factors (i.e. ratio of metal concentrations in leaves and in soils) which reached 2 and 1.2 for Cd and Zn, respectively. Our study confirmed that Salicaceae items were obviously important contributors of Cd and, to a lesser extent of Zn, to wood mice Furthermore, our results demonstrated that high Cd concentration due to Salicaceae items was "diluted" by other plant items, potentially less Cd accumulators than Salicaceae items. Such phenomenon was postulated by Boyd [START_REF] Boyd | Hyperaccumulation as a Plant Defensive Strategy[END_REF] under the name of "diet dilution hypothesis": trophic exposure of vertebrate herbivores to TMs by high metal accumulator plants would be diluted by consuming low accumulator resources. Indeed, high diet richness of plant might lead to dilute the proportion of Salicaceae items in SCs in our analyses. While this possibility has been suggested in other field studies (e.g. Martens and Boyd [START_REF] Martens | The Defensive Role of Ni Hyperaccumulation by Plants: A Field Experiment[END_REF] ), we clearly demonstrated, to our knowledge for the first time, the validity of this hypothesis under field conditions. However, we failed to identify potential contributor of exposure to Pb. Migeon et al. [START_REF] Migeon | Metal Accumulation by Woody Species on Contaminated Sites in the North of France[END_REF] demonstrated an interspecific variation of Pb concentrations in plant tissues over our study area, although less contrasted than for Cd or Zn. One possibility could be an effect of soil ingested by mice. Quantity of soil ingested by small rodents represents only a few percent of dry mass in the diet, which was quantified to be maximum of 2% for the white-footed mouse. [START_REF] Beyer | Estimates of Soil Ingestion by Wildlife[END_REF] Lead bioconcentration factors of woody plants in our study area were very low, ranging from 0.005 to 0.11, [START_REF] Migeon | Metal Accumulation by Woody Species on Contaminated Sites in the North of France[END_REF] suggesting a low mobility of Pb towards plants. The same pattern was observed over this area for invertebrates (earthworms, mollusks, arthropods) which exhibited low biota-to-soil concentration ratios for Pb (mostly lower than 0.1, and lower than for Cd and Zn). [START_REF] Fritsch | Influence of Landscape Composition and Diversity on Contaminant Flux in Terrestrial Food Webs: A Case Study of Trace Metal Transfer to European Blackbirds Turdus Merula[END_REF][START_REF] Mourier | Chemical Extractions and Predicted Free Ion Activities Fail to Estimate Metal Transfer from Soil to Field Land Snails[END_REF] It is thus possible that a large part of Pb in SCs could be derived from soils and that it may obscure any effect of dietary richness or composition on Pb concentration in SCs. Effects of geophagy on exposure to TM and/or accumulation of TM in animals remain as a further issue that undoubtedly deserves attention.

On the other hand, consuming invertebrate items, especially earthworms that are well-known as high TM accumulator species, [START_REF] Nahmani | A Review of Studies Performed to Assess Metal Uptake by Earthworms[END_REF] did not increase the exposure of wood mice to TMs in our study. Accumulation patterns in earthworms exposed to soils can be strongly modified by environmental factors like pH or organic matter content in soils, [START_REF] Janssen | Equilibrium Partitioning of Heavy Metals in Dutch Field Soils. II. Prediction of Metal Accumulation in Earthworms[END_REF][START_REF] Morgan | Earthworms as Biological Monitors of Cadmium, Copper, Lead and Zinc in Metalliferous Soils[END_REF][START_REF] Nannoni | Soil Properties and Metal Accumulation by Earthworms in the Siena Urban Area (Italy)[END_REF] which may explain the discrepancy between our results and the literature emphasizing the importance of earthworms on the exposure of mammals to TMs. [START_REF] Rogival | Transfer and Accumulation of Metals in a Soil-dietwood Mouse Food Chain along a Metal Pollution Gradient[END_REF][START_REF] Van Den Brink | Effects of Soil Properties on Food Web Accumulation of Heavy Metals to the Wood Mouse (Apodemus Sylvaticus)[END_REF][START_REF] Ma | Heavy Metal Accumulation in the Mole, Talpa Europea, and Earthworms as an Indicator of Metal Bioavailability in Terrestrial Environments[END_REF] Further, trophic availability of metals accumulated in the various diet items can be different according to their sequestration forms in item tissues, which may also or rather lead to such discrepancies with literature. [START_REF] Vijver | Internal Metal Sequestration and Its Ecotoxicological Relevance: A Review[END_REF] Moreover, some invertebrates show high metal accumulation capacity, e.g. snails, isopods, or spiders, [START_REF] Hopkin | Ecophysiology of Metals in Terrestrial Invertebrates[END_REF][START_REF] Janssen | Comparison of Cadmium Kinetics in Four Soil Arthropod Species[END_REF][START_REF] Dallinger | Strategies of Metal Detoxification in Terrestrial Invertebrates[END_REF][START_REF] Heikens | Bioaccumulation of Heavy Metals in Terrestrial Invertebrates[END_REF][START_REF] Hendrickx | Absence of Cadmium Excretion and High Assimilation Result in Cadmium Biomagnification in a Wolf Spider[END_REF] whereas others, e.g. Coleoptera, [START_REF] Heikens | Bioaccumulation of Heavy Metals in Terrestrial Invertebrates[END_REF] accumulate less metals in their tissues. For instance, Fritsch et al. [START_REF] Fritsch | Influence of Landscape Composition and Diversity on Contaminant Flux in Terrestrial Food Webs: A Case Study of Trace Metal Transfer to European Blackbirds Turdus Merula[END_REF] showed that trophic transfer of TMs to blackbirds Turdus merula was less important in habitats linked with a diet based on earthworms than in habitats linked with a diet based on other invertebrates in the surroundings of Metaleurop Nord. They also demonstrated close to or even higher transfer factors (i.e. metal concentration in items divided by metal concentration in soils) of some other invertebrate taxa than earthworms for Cd and Pb. In the present study, invertebrate items could not be specified further than large categories like "arthropods and mollusks" or "earthworms", and our low taxonomic resolution of invertebrate items could make unclear the role of animal matters on the TM exposure of mammals.

Few studies have focused on the relationship between exposure to TMs and diet richness. To our knowledge, only Orłowski et al. [START_REF] Orłowski | Relationships between Stomach Content and Concentrations of Essential and Non-Essential Elements in Tissues of Omnivorous Nestling Rooks Corvus Frugilegus: Is the Size and Composition of Stomach Content Relevant[END_REF] showed significant correlations between the number of items in the diet of nestling rooks Corvus frugilegus and accumulation of TMs in their tissues. In that study, Pb concentrations in the liver were negatively correlated with the number of vegetal items, while Cd concentrations were positively correlated with the number of animal items. However, oral exposure to TMs is not always correlated with TM accumulation in tissues. In the study of Godwin et al. [START_REF] Godwin | Metals and Metalloids in Nestling Tree Swallows and Their Dietary Items near Oilsands Mine Operations in Northern Alberta[END_REF] for example, TM concentrations in stomach contents and in tissues of tree swallow Tachycineta bicolor nestlings of the age of 14 days showed no correlation for Ni, Sr, and Zn, negative correlations for Mo and Cu, and correlations depending on year and/or tissue for other elements including Cd and Pb. TM accumulation in animal tissues is determined by several other factors than oral exposure, like species, age, sex and exposure by other routes. [START_REF] Gall | Transfer of Heavy Metals through Terrestrial Food Webs: A Review[END_REF] Assimilation of elements via oral route is modulated in intestines by composition of micro-and macro-nutrients, such as iron or calcium. [START_REF] Shore | Ecotoxicology and Environmental Toxicology Series[END_REF][START_REF] Nordberg | Handbook on the Toxicology of Metals[END_REF] Metal speciation is also another factor affecting intestinal assimilation of TMs. Chunhabundit et al. [START_REF] Chunhabundit | Cadmium Bioavailability from Vegetable and Animal-Based Foods Assessed with in Vitro Digestion/Caco-2 Cell Model[END_REF] Even though an important functional role of diet richness on the exposure to TMs is shown here, further investigations should be carried out for elucidating the variation of the oral exposure in space and time. First, metal concentrations in both invertebrates and plants are largely variable according to their organ, size, season, age and/or developmental stage, [START_REF] Hare | Aquatic Insects and Trace Metals: Bioavailability, Bioaccumulation and Toxicity[END_REF][START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF] which are undetectable by DNA based method. Although the metabarcoding is a powerful food identification technique compared to the classical microscopic method or the nitrogen and carbon isotopic approach, accessing information other than taxonomy of items, such as quantity ingested, organs consumed and their TM concentrations, should be included in further studies.

Second, mechanisms underlying the relationship between the diet dilution and the diversity of available food in the field remain unclear. The causes and consequences of food composition and food selection depend on the foraging behavior of a given animal, which is also determined by several factors such as balance between nutrients and toxins in food or avoidance of risks by parasites and/or predators. [START_REF] Singer | Understanding Omnivory Needs a Behavioral Perspective[END_REF] In our study, occurrences in SCs of Salicaceae and Sapindaceae, two plant families preferentially consumed by wood mice, [START_REF] Ozaki | Does Pollution Influence Small Mammal Diet in the Field? A Metabarcoding Approach in a Generalist Consumer[END_REF] were likely to switch along the gradient of soil contamination: Occurrence in SCs of Salicaceae items decreased, whereas occurrence of Sapindaceae items increased. This result could be due to a change in the preference of wood mice for Salicaceae items along the gradient of soil TM contamination. [START_REF] Ozaki | Does Pollution Influence Small Mammal Diet in the Field? A Metabarcoding Approach in a Generalist Consumer[END_REF] The composition of available food in the field and the soil TM contamination may also be considered as to be important factors determining the foraging behavior. Although our finding supported the diet dilution hypothesis, the generality of this phenomenon can depend on several biological and environmental conditions, which should be studied further.

To conclude, the level of exposure of small mammals to TMs is influenced not only by the presence of certain items in the diet but also by the diet richness. The functional role of the diet richness on exposure to TMs observed in the present study concurs with the diet dilution hypothesis. Challenging such hypotheses about the functional role of biodiversity on the trophic transfer of pollutants and investigating trait-based mechanisms are possible if ingested food is identified in details. Although the metabarcoding approach allows identifying the taxonomy of food under field conditions, associating life history traits and chemical composition of each item and clarifying mechanisms of foraging behavior of animals will improve chemical risk assessment in wildlife. Further perspectives also deal with the quantification of trophic links to address the functional significance of trophic interactions for the transfer of matter, energy, and essential elements versus the transfer of toxicants, and the evolutionary trade-offs between acquiring nutrients but being exposed to toxic substances.

  reported higher intestinal assimilation of Cd in human intestinal cell from animal-based food than from vegetal-based food. Taking into account those nutrient factors modulating bioavailability of TMs, diet diversity and composition possibly play different roles over the exposure to TMs and the TM accumulation.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Conditional inference trees (CIT) for (a) the exposure to Cd and (b) the exposure to

*

  Phone number: +33 (0)3 81 66 65 98; E-mail address: shinji.ozaki@univ-fcomte.fr.Notes# both authors contributed equally to supervising this work ACKNOWLEDGMENT This study was financially supported by the project BIOTROPH, co-funded by the Agence De l'Environnement et de la Maîtrise de l'Energie (ADEME; contract No.1172C0030) and the Conseil Régional du Nord-Pas de Calais (CRNPC; orders No.12000921 and 14001044; joint call with the Fondation pour la Recherche sur la Biodiversité). The first author was financially supported by a grant from the Conseil Régional de Franche-Comté (contract No. 2015C-06107).

The authors gratefully thank Cécile Grand from ADEME for fruitful scientific discussion. We also thank Eva Bellemain and Alice Valentini from SPYGEN Company for their help in DNA analyses and interpretations. We finally thank Nadia Crini, Caroline Amiot, Dominique Rieffel and Anne-Sophie Prudent for their precious assistance. AUTHOR CONTRIBUTIONS F.R. and R.S. conceived the study; S.O., R.S., C.F. and F.R. designed the study; R.S., C.F., F.M., T.C. and F.R. performed field samplings and laboratory treatments; B.V. performed bioinformatics treatments; S.O. performed the statistical analyses; S.O., F.R., R.S. and C.F. interpreted the results. S.O. wrote the manuscript under the supervision of all authors.

Supporting Information

Table S1: Information about each study site about TM contamination and landscape dominant habitats.

Table S2: List of primers used for identifying food.

Table S3: List of frequency of occurrence of items in SCs.

AUTHOR INFORMATION

Corresponding Author


Binghua Ma

Walid Baaziz

Léo Mazerolles

Ovidiu Ersen

Bernard Sahut

Clément Sanchez

Stéphane Delalande

David Portehault

Liquid Processing of Bismuth-Silica Nanoparticle/Aluminum Matrix Nanocomposites for Heat Storage Applications

Metal matrix nanocomposites encompassing low-melting point metal nano-inclusions are promising candidates for thermal regulation of devices at high temperature. They are usually processed by solid-state routes that provide access to a limited range of materials and are hardly compatible with complex shaping processes and with large-scale applications. Herein, we develop a liquid-phase processing technique to design aluminum matrix nanocomposites made of phase change nanoparticles, using bismuth nanoparticles as a proof-of-concept. The bismuth nanoparticles derived from colloidal chemistry are first encapsulated in a silica shell and then dispersed by ultrasonication into molten aluminum. Using X-ray diffraction, electron microscopy, and X-ray photoelectron spectroscopy, we probe the evolution of the bismuth particles and of the inorganic shell. We demonstrate that the silica shell acts as a barrier against extensive coalescence of particles during the dispersion process, thus enabling a decrease and a widening of the phase change temperature range. aluminum, composites, nanoparticles, core-shell, bismuth, heat storage, phase change

INTRODUCTION

Phase change materials (PCMs) display a promising range of applications for thermal control of electronic devices [START_REF] Wu | Heat Dissipation Design for Lithium-Ion Batteries[END_REF] and for flame retardants [START_REF] Qu | Numerical Model of the Passive Thermal Management System for High-Power Lithium Ion Battery by Using Porous Metal Foam Saturated with Phase Change Material[END_REF][START_REF] Alipanah | Numerical Studies of Lithium-Ion Battery Thermal Management Systems Using Phase Change Materials and Metal Foams[END_REF][START_REF] Wu | An Experimental Study of Thermal Management System Using Copper Mesh-Enhanced Composite Phase Change Materials for Power Battery Pack[END_REF] by absorbing and releasing heat upon melting and solidifying, respectively. Compared to air and liquid cooling or to heat pipes, PCMs use less power and require less complex thermal system design. [START_REF] Offer | Cool Metric for Lithium-Ion Batteries Could Spur Progress[END_REF] Enhancing the heat management efficiency of PCMs and their response time requires a significant increase in their thermal conductivity. [START_REF] Jaguemont | Phase-Change Materials (PCM) for Automotive Applications: A Review[END_REF] This aim can be reached by embedding phase change inclusions within metal matrices that are much more thermally conductive than common polymer matrices and also offer higher mechanical strength. [START_REF] Jaguemont | Phase-Change Materials (PCM) for Automotive Applications: A Review[END_REF] The development of composites made of metallic PCM inclusions into metallic matrices would provide access to temperature ranges that are not accessible to organic PCMs, [START_REF] Shchukina | Nanoencapsulation of Phase Change Materials for Advanced Thermal Energy Storage Systems[END_REF] thus paving the way to other fields of applications, such as the automotive field. Using PCM metal or alloy nanoparticles and their related depression of melting point versus bulk materials would offer an additional degree of freedom by enabling to tune the phase change temperature (PCT) not only with the composition but also with the particle size. [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] These metal matrix nanocomposites are, however, limited by their fabrication. To ensure dispersion of the inclusions, they are produced either by powder metallurgy [START_REF] Estruga | Ultrasonic-Assisted Synthesis of Surface-Clean TiB 2 Nanoparticles and Their Improved Dispersion and Capture in al-Matrix Nanocomposites[END_REF][START_REF] Abraham | Energy Storage Materials with Oxide-Encapsulated Inclusions of Low Melting Metal[END_REF] or by chemical synthesis of the matrix, [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] which are not adapted to large-scale applications. [START_REF] Estruga | Ultrasonic-Assisted Synthesis of Surface-Clean TiB 2 Nanoparticles and Their Improved Dispersion and Capture in al-Matrix Nanocomposites[END_REF] Dispersion of PCM nanoscaled inclusions into metal matrices by liquid metallurgy would offer access to a wide range of materials with high versatility in the choice of the materials and of the PCT. It would also enable coupling with a variety of liquid-based shaping processes and would deliver materials relevant at the industrial scale. However, the targeted PCTs are usually below the melting point of the metal matrix. Therefore, producing metal matrix PCM nanocomposites by liquid metallurgy is a great challenge as it means dispersing liquid metal droplets in another liquid metal, at temperatures usually above 600 °C, while avoiding coalescence of the PCM inclusions and/or alloying with the matrix. Melt spinning has been proposed to quench the coalescence of particles precipitated in the liquid phase by fast cooling when the matrix is an amorphous alloy. [START_REF] Goswami | Depression of Melting Point of Multidomained Bismuth in Aluminum Based Metallic Glass Nanocomposites[END_REF] The method cannot be applied to bulk crystalline matrices. As a result, no crystalline metal matrix PCM composite, containing either nano-or micro-inclusions, has been reported from liquid metallurgy processes.

To address this challenge and enable liquid processing of metallic phase change nanocomposites, we propose to disperse colloidal chemistry-derived metal PCM nanoparticles in a molten metal by using ultrasonication [START_REF] Mohanty | Ultrasonic Cavitation: An Approach to Synthesize Uniformly Dispersed Metal Matrix NanocompositesA Review[END_REF] in the melt at high temperatures (Figure 1, pathway A). To enhance the dispersion and the stability over thermal cycling of the nanoparticles (Figure 1, pathway B), we encapsulate them in a silica shell prior to incorporation into the liquid metal. During incorporation, the shell is expected to react in situ with the metal matrix through the aluminothermal reduction. [START_REF] Deqing | Aluminothermic Reduction of Silica for the Synthesis of Alumina-Aluminum-Silicon Composite[END_REF] We study herein the evolution of this shell, its impact on the nanostructure of the nanocomposites and on their phase change energy storage properties. We show that this procedure provides sustainable phase change properties over thermal cycling by maintaining the nanosize of the metal cores. We use bismuth nanoparticles as proof-of-concept phase change inclusions for their low-melting point and aluminum as a light weight, high thermal conductivity matrix.

RESULTS AND DISCUSSION

Synthesis and Characterization of Bi@SiO 2 Nanostructures. We have prepared bismuth nanoparticles according to the colloidal synthesis proposed by Scheele et al. [START_REF] Scheele | Synthesis and Thermoelectric Characterization of Bi 2 Te 3 Nanoparticles[END_REF] where bismuth acetate was reduced in oleylamine to form bismuth nanoparticles with 1-dodecanethiol ligands. X-ray diffraction (XRD) confirms (Figure S1) the formation of bismuth, and transmission electron microscopy (TEM) (Figure 2a) shows that the particles exhibit a narrow size distribution centered at around 25 nm. In the second step, the bismuth nanoparticles were coated with a silica shell by adapting the Stober method to ensure heterogeneous nucleation at the surface of the particles. [START_REF] Wang | Silica-Based Nanocomposites via Reverse Microemulsions: Classifications, Preparations, and Applications[END_REF][START_REF] Stöber | Controlled Growth of Monodisperse Silica Spheres in the Micron Size Range[END_REF] TEM indicates a 7 nm thick shell with a contrast lower than the bismuth core (Figure 2b). The silica shell actually embeds several particles, forming aggregates with size up to 160 nm. The dispersion state within the aggregates was quantified by the projected area distribution (Figure 2c), which shows that the silica shells embed from single to about 20 bismuth cores. Although significant aggregation is observed, the dispersion state is enhanced compared to previous methods using surfactants, [START_REF] Zhao | A Biocompatible Theranostic Agent Based on Stable Bismuth Nanoparticles for X-Ray Computed Tomography/Magnetic Resonance Imaging-Guided Enhanced Chemo/Photothermal/Chemodynamic Therapy for Tumours[END_REF] instead of strongly bound thiol surface ligands.

To evaluate the efficiency of the silica shell in preventing coalescence of the bismuth cores, we have heated the coreshell nanostructures and sole bismuth nanoparticles above the melting point of bulk Bi (271 °C). When heated at 320 °C, the bare Bi particles undergo extensive coalescence and growth (Figure S2). On the contrary, the general appearance of the Bi@SiO 2 nanostructures was preserved at 350 °C (Figure 2d), and the size of the Bi cores only slightly increased for the cores embedded in the same silica shell. Thus, the silica shell preserves the separated Bi cores from coalescence.

To provide additional information on coalescence during heating and cooling, in situ TEM experiments were carried out on Bi@SiO 2 nanostructures by heating to 350 °C at 10 °C min -1 . Scanning transmission electron microscopy-high-angle annular dark-field imaging (STEM-HAADF) images (Figure S3) show that the morphology of the nanoparticles was retained during heating and cooling, with a contrast revealing the clear edge of overlapped nanoparticles. The dependency of the coalescence behavior versus the aggregation state inside the silica shells has been further studied by in situ TEM (Figure S4). Above the melting point of Bi cores at around 250 °C, Bi cores embedded in the same silica shell and in contact with each other coalesce irreversibly (Figure S4, orange rectangle), while Bi cores initially separated with a silica layer from each other remain dispersed upon heating and cooling. Although Bi cores initially embedded together coalesce, they still remain trapped inside the shell over the melting point, thus making feasible the incorporation of Bi@SiO 2 nanostructures into molten aluminum.

Bi@SiO 2 -Al Nanocomposites. Nanocomposites made of an aluminum matrix embedding Bi@SiO 2 (Bi@SiO 2 -Al) were synthesized by ultrasound-assisted liquid metallurgy. To ensure the full incorporation of the nanoparticles into molten aluminum, a pellet of Bi@SiO 2 nanostructures (0.25 at %) in aluminum was prepared by pressing Bi@SiO 2 and aluminum powders pre-dispersed into ethanol (see details in the experimental method section). The pellet was then incorporated into molten aluminum at 740 °C by ultrasound treatment in an induction furnace. The resulting samples showed metallic aluminum appearance (Figure S5). The Bi/Al ratio was evaluated by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF) on the top and bottom of each sample and on middle slice cuts of the monoliths. The constant ratio of 0.3 at % in all pieces is consistent with the initial precursor ratio, indicating that all the nanostructures were incorporated and are homogeneously distributed into the whole volume of the monoliths.

Scanning electron microscopy (SEM) imaging in the backscattered electron mode (Figure 3a) highlights white areas corresponding to electron-rich bismuth, with a size up to 120 nm. The TEM images reveal 10 nm (Figure 3b,c) and 60 nm (Figure 3d) bismuth nanoparticles, well crystallized in the Bi hexagonal structure according to high-resolution TEM (HRTEM) (Figure 3c inset) and selected area electron diffraction (SAED) (Figure 3d inset) performed on the larger particles. HRTEM (Figure 3c) also highlights atomic planes of the Al matrix and does not indicate any intermediary phase between the matrix and the nanoparticles. Larger particles around 120 nm were also observed by STEM-HAADF (Figure S6). The size distributions evaluated by SEM and TEM highlight two distinct populations, centered, respectively, at ca. 60 and 8 nm. According to TEM, particles below 10 nm were isotropic, while the shape of particles above 30 nm was irregular. Therefore, SEM and TEM evidence a widening of the size distribution from initial 25 nm Bi nanoparticles toward both smaller and larger particles. This behavior is indicative of Ostwald ripening during the incorporation process, a phenomenon in which bismuth from the smaller particles dissolve in the liquid medium and redeposit on larger particles. This hypothesis is supported by the slight but significant solubility of Bi in Al at 700 °C of about 1.5 wt %. [START_REF] Manasijevic | Experimental Investigation and Thermodynamic Prediction of the Al-Bi-In Phase Diagram[END_REF] Note that sonication may also exacerbate Ostwald ripening, as already described on polymer composites. An area containing a large coalesced bismuth object was mapped by the STEM-energy-dispersive system (EDS) (Figure 4a-f). Bi was surrounded by an O-rich area also containing Al but exempt of Si (orange square, Figure 4a). This area corresponds (orange square, Figure 5a,b) to an Al/ O-rich rod-like region with non-uniform contrast, which highlights crystal defects. The interplanar distances measured by HRTEM are consistent with Al 2 O 3 (100) planes. Therefore, bismuth is surrounded by alumina.

The Si map (Figure 4f) confirms that silicon was no longer around Bi particles and segregated into Bi-free areas (Figures 4 and 5a green square, 5d violet square). The low overlap between Si-rich and O-rich regions (Figure 4f,e) indicates the formation of low oxidation state silicon species. We observed two types of regions in Si-rich area: an amorphous-like (Figure 5a green square and 5c) region and a crystalline region (Figures 5d violet square and5e). HRTEM analysis and the corresponding FFT of the crystalline part (Figure 5e) are fully consistent with the structure of sillimanite Al 2 SiO 5 . We discuss below the origin of these different components, which are indicative of the reaction mechanisms occurring during incorporation of the nanoparticles into the aluminum melt.

The presence of alumina, low oxidation state silicon species, and the aluminosilicate sillimanite is consistent with aluminothermic reduction of the silica shell of the Bi@SiO 2 nanostructures, yielding aluminosilicates as reaction intermediates. [START_REF] Deqing | Aluminothermic Reduction of Silica for the Synthesis of Alumina-Aluminum-Silicon Composite[END_REF] In order to further confirm this reaction pathway, the redox processes involving the aluminum matrix and the silica shell were probed on a slice cut piece of nanocomposites analyzed by X-ray photoelectron spectroscopy (XPS) in the Si 2p (Figure 6a) and Si 2s (Figure 6b) regions. The spectrum of pure aluminum was also recorded as a reference. The Al plasmonic peak overlaps with the Si 2p binding energy regions between 99 and 102 eV (Figure 6a), but the Si 2s area enables to decipher the Al plasmon peak at 147.4 eV from the Si 2s signal at about 150-155 eV (Figure 6b). For the Bi@SiO 2 -Al composite, the most intense peak at 155.1 eV accounts for the Si-O bonds of SiO 2 . [START_REF] Barr | An XPS Study of Si as It Occurs in Adsorbents, Catalysts, and Thin Films[END_REF] An additional contribution between 150 and 154 eV is not observed for pure aluminum and is ascribed to other silicon species at lower binding energy than that for SiO 2 , which is consistent with low oxidation state Si species and/or an aluminosilicate. [START_REF] Deqing | Aluminothermic Reduction of Silica for the Synthesis of Alumina-Aluminum-Silicon Composite[END_REF] According to XPS and STEM-EDS (Figures 4a,b and5), we assign the amorphous areas to low oxidation state silicon. Because oxidation of low oxidation state Si species could occur upon exposure to air during transfer of the sample to the XPS chamber, the pristine samples could exhibit a higher portion of reduced silicon than indicated by XPS analysis.

The reduction of SiO 2 particles (about 30 μm) by molten aluminum has been studied by Deqing and Ziyuan. [START_REF] Deqing | Aluminothermic Reduction of Silica for the Synthesis of Alumina-Aluminum-Silicon Composite[END_REF] In the Al-SiO 2 system, the reduction of silica by aluminum is thermodynamically favorable (reaction 1 below). Once alumina is formed, it can react with remaining silica and then form mullite or other aluminosilicates (reaction 2), evolving finally toward alumina and silicon (reaction 3).

+ → + 4Al 3SiO 2Al O 3Si 2 2 3
(1)

+ → 3Al O 2SiO Al Si O 2 3 2 6 2 13
(2)

+ → + 8Al 3Al Si O 13Al O 6Si 6 2 13 2 3 (3)
Deqing and Ziyuan 13 also observed the formation of a 5 μm thick shell of alumina surrounding unreacted ∼20 μm silica particles after 30 min of reaction at 700 with 5 °C min -1 heating rate, while silicon had migrated within the aluminum matrix and formed rod-like Si inclusions. In our study, the silica component is present in the silica shell with a thickness of 7 nm, 3 orders of magnitude lower than previously studied, [START_REF] Deqing | Aluminothermic Reduction of Silica for the Synthesis of Alumina-Aluminum-Silicon Composite[END_REF] which supports the reduction of silica to a larger extent at a similar temperature. Nonetheless, aluminothermic reduction is not complete as we observe, besides amorphous low oxidation state silicon, an intermediate Si(IV)-containing aluminosilicate. This behavior can be attributed to short reaction times, as our experiments rely on induction heating at a rate of 350 °C min -1 and reaction time of 10 min. We have observed the formation of sillimanite (Al 2 SiO 5 ) as the intermediate phase. Generally, sillimanite forms above 776 °C, [START_REF] Salje | Heat Capacities and Entropies of Andalusite and Sillimanite: The Influence of Fibrolitization on the Phase Diagram of the Al 2 SiO 5 Polymorphs[END_REF] corresponding to 40 °C over our synthesis temperature. This high-temperature phase may be induced by intense local heating provided by ultrasound treatment. [START_REF] Suslick | Acoustic Cavitation and Its Chemical Consequences[END_REF] Overall, we propose the following reaction mechanism: Bi nanoparticles were partially coalesced inside the silica shell during the heating ramp. The pristine silica shell was reduced by aluminum that is concomitantly oxidized and reacts with remaining silica to form an aluminosilicate intermediate. During ultrasound-assisted heating for the nanocomposite preparation, the silica shell separates from the Bi core, leading to a leaching of Bi droplets which then undergo Ostwald ripening and develop into two populations.

Bi-Al Nanocomposites. To probe the impact of the silica shell on the dispersion of Bi nano-objects, we have incorporated Bi nanoparticles without silica into molten aluminum, as described above for Bi@SiO 2 -Al composites. The as-obtained Bi-Al composite exhibits also an evolution of Bi objects into two populations (Figure 7), one around one micrometer and another one around 5-30 nm. Bismuth then underwent Ostwald ripening, as with the silica shell, but the population of large objects showed significantly larger size, losing the nanoscale, when no silica was used. Therefore, although the silica shell in Bi@SiO 2 -Al composites underwent chemical reaction and local mobilization, it acted efficiently against coalescence of the cores during incorporation into molten aluminum. Thus, the Bi@SiO 2 -Al nanocomposite exhibited a wide size distribution at the nanoscale.

Thermal Properties of Bismuth and Bi@SiO 2 Nanoparticles. Differential scanning calorimetry (DSC) was performed for both precursor Bi, Bi@SiO 2 nanoparticles, and the corresponding nanocomposites. Figure 8a,b displays the thermal behavior at the third heating-cooling cycle for pristine Bi@SiO 2 and Bi nanoparticles, respectively. Bi@SiO 2 particles exhibit an endothermic peak at ∼242 °C, which is in agreement with the depression of the melting point (271 °C for bulk Bi) with particle size decrease. [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] The melting point measured herein for 25 nm silica-coated Bi nanoparticles is in accordance with the results obtained for Bi-carbon nanocomposites. [START_REF] Tran | Metal Nanoparticle -Carbon Matrix Composites with Tunable Melting Temperature as Phase-Change Materials for Thermal Energy Storage[END_REF] Bi@SiO 2 nanoparticles exhibit reversible thermal properties over four cycles (Figure 8a). On the contrary, the bare bismuth nanoparticles (Figure 8b) display three sharp endothermic peaks at ∼263 and 271 °C, the latter corresponding to bulk Bi melting. The first two endothermic peaks are attributed to the melting temperature depression with the particle size. [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] The appearance of the peaks at higher temperature indicate that the nanoparticles coalesced, and large particles up to bulk were formed. Therefore, the silica shell was necessary to maintaining the nanoscale and the melting point depression of Bi nanostructures. These results are consistent with TEM observations after heating (Figure S2).

Thermal Properties of Nanocomposites Encompassing Phase Change Bismuth Inclusions. The DSC curves for the Bi@SiO 2 -Al composites (Figure 8c,d) also show endothermic peaks during heating and an exothermic broad peak during cooling, indicating solidification of undercooled bismuth. The high-temperature region of the DSC curves (Figure 8c) shows that the melting behavior significantly differs from those of the initial particles described above. The Bi@SiO 2 -Al nanocomposite exhibits two endothermic peaks: a broad melting peak at 263 °C with the onset temperature at 251 °C, corresponding to nanoparticles melting, and a low intensity sharper peak at 271 °C, corresponding to bulk bismuth. The shift of these peaks to higher temperatures compared to unreacted Bi@SiO 2 nanoparticles confirms that the nanoparticles coalesced during the incorporation of the particles into the melt, but a significant fraction still shows melting point depression, indicating that nanoscale objects have been maintained. This is consistent with the remaining single Bi nanoparticles or aggregates of only few Bi cores within the initial nanocomposite, as described above (Figures 3 andS6). The enthalpy ratio for the peaks at 263 and at 271 °C is 9:1, indicating that around 90% of Bi element are still nanoscaled, with the assumption that the melting latent heat of nanoscale Bi is close to the value for bulk Bi. Because the melting latent heat decreases with the particle size, [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] one can assume that the molar fraction of bismuth at the nanoscale is above 90%. The heat absorption ability evaluated by the enthalpy of the melting process was 0.38 J g -1 , close to the expected value based on the chemical composition at 0.47 J g -1 using the melting latent heat of bulk Bi. DSC over 15 consecutive heating-cooling cycles between 20 and 300 °C showed the cycling ability of the Bi core after melting as the DSC curves and the latent heat remained unchanged during cycling. The silicon species are considered stable after cycling as the aluminosilicate phases are stable, [START_REF] Althaus | The Triple Point Andalusite  Sillimanite  Kyanite[END_REF] and no reaction is expected between Al and Bi 26 on the one side and Al and Si 27 on the other side. Besides, thermal cycling is performed at temperatures much lower than the preparation temperature in molten aluminum, so that any structural changes are unlikely.

The heat absorption ability of the as-prepared Bi@SiO 2 -Al nanocomposite is low compared to Bi@Al 2 O 3 -Al prepared by powder metallurgy [START_REF] Khan | A Review of Performance Enhancement of PCM Based Latent Heat Storage System within the Context of Materials, Thermal Stability and Compatibility[END_REF] because we used a low Bi content material as a proof-of-concept. The heat storage capacity may be improved by increasing the amount of inclusion particles within the matrix. We did not detect by DSC the phase change of small nanoparticles around 8 nm expected from TEM observations, which occurred at around 240 °C in an Ag matrix. [START_REF] Liu | Metal Matrix-Metal Nanoparticle Composites with Tunable Melting Temperature and High Thermal Conductivity for Phase-Change Thermal Storage[END_REF] This could be explained by the decreased enthalpy with the particle size [START_REF] Scheele | Synthesis and Thermoelectric Characterization of Bi 2 Te 3 Nanoparticles[END_REF][START_REF] Fu | Size-and Shape-Dependent Melting Enthalpy and Entropy of Nanoparticles[END_REF] or the low volume fraction of nanoparticles smaller than 10 nm. DSC further confirms the efficient role of the silica shell to limit the coalescence of Bi nanoparticles since the Bi-Al nanocomposite showed three endothermic peaks at 262, 269, and 271 °C overlapped together and shifted to higher temperature compared to the Bi@SiO 2 -Al nanocomposite, which implies the consecutive coalescence of nanoparticles into bulk objects during cycling.

Compared to a Bi-Al composite prepared by powder metallurgy [START_REF] Abraham | Energy Storage Materials with Oxide-Encapsulated Inclusions of Low Melting Metal[END_REF] at the same heating rate and with micron and nano-sized bismuth inclusions, the distribution of melting temperatures for Bi@SiO 2 -Al prepared by our liquid metallurgy process is broader and shifted to lower temperatures. This demonstrates a decreased Bi particle size achieved through nanoparticle encapsulation. The small initial nanoparticle size, the protective silica shell, the ultrasound treatment inducing disruption of Bi droplets, and the rapid thermal treatments by induction heating are keys to achieve small inclusion nanoparticles with a wide phase transition temperature range, which can be a considerable advantage to ensure progressive heat absorption over a wide temperature range.

CONCLUSIONS

We have developed an ultrasound-assisted liquid metallurgy process to phase change metal matrix nanocomposites, by encapsulating pre-made metal nanoparticles within a silica shell. This shell acts as a barrier against extensive coalescence of the metal cores, which are molten during the incorporation process. This process relies on preliminary synthesis of nanoparticles, which ensures the suitability of the method for the very wide range of metal and alloy nanomaterials accessible by rich colloid chemistry. Hence, we believe that the process opens the way to a wide diversity of lightweight aluminumbased phase change materials that could be shaped according to liquid metal processing. Therefore, various fields of application can be targeted, including thermal regulation in automobiles. We also expect the mechanical properties of the resulting nanocomposites to be influenced by phase change inclusions. This point will be addressed in the future.

EXPERIMENTAL METHODS

Materials. Bismuth acetate (99.99%), oleylamine (technical grade, 70%), 1-dodenanethiol (98%), tetraethyl orthosilicate (TEOS, 99.99%), ammonia aqueous solution (28-30%), and aluminum powder (99.99%, 320 mesh) were all purchased from Sigma-Aldrich. Aluminum beads (99.9%, around 3 mm diameter) were obtained from Alfa Aesar. The reagents were used as received.

Synthesis of Bismuth Nanoparticles and Silica-Coated Bismuth Nanoparticles. Bismuth nanoparticles were prepared by colloidal synthesis in a mixture of organic solvents based on a previous published protocol. [START_REF] Scheele | Synthesis and Thermoelectric Characterization of Bi 2 Te 3 Nanoparticles[END_REF] Bismuth acetate (1 mmol) was mixed with 1dodecanethiol (11.1 mL) and heated to 45 °C for 45 min under vacuum. The reaction flask was purged with N 2 and heated to 60 °C. Oleylamine (22.2 mL) at room temperature was quickly added under stirring, and the reaction mixture was further heated for 24 h. Finally, Bi nanoparticles were washed two times by ethanol/chloroform (20/1 vol/vol) and re-dispersed in ethanol. The silica-coated bismuth nanoparticles were prepared according to the Stober method as follows:15 TEOS was dissolved in absolute ethanol at a concentration of 0.38 M. 10 μL of TEOS solution and 100 μL of ammonia 30% were subsequently added to the 1 mL of alcoholic bismuth suspension [Bi] = 6 × 10 -4 M under vigorous stirring. The mixture was heated at 40 °C for 3 h followed by washing by two cycles of centrifugationredispersion in ethanol. The final powder was dried in a vacuum oven at 40 °C for 16 h. In order to evaluate the thermal stability, 20 mg of Bi@SiO 2 nanoparticles was annealed in a Naberthem chamber furnace under air condition for 1 h dwell time with a heating rate of 10 °C min -1 .

Synthesis of Bi-Al and Bi@SiO 2 Nanocomposites. Bi-Al nanocomposites were prepared by ultrasound-assisted casting of a mixture composed of nanoparticles and aluminum in an induction oven. Briefly, 2 g of aluminum powders and alcoholic bismuth suspension were mixed by sonication at the boiling point for 1 h to get a ratio of 0.3 at % between Bi and Al. Then, the mixture was dried in a vacuum oven at 40 °C for 1 h, followed by pressing into a 13 mm pellet under 8 tons for 2 min. Subsequently, the pellet and 0.4 g of pure Al beads were heated into an alumina crucible using an induction oven (CEIA, power cube 90 series 200) at 740 °C under an argon atmosphere. After dwelling for 1 min, an ultrasound power of 80 Watts (QSonica, Q500A-220, USA) was applied through a sonication horn (niobium tip with 4 mm diameter) to disperse the nanoparticles for 5 min. The final composite was obtained by cooling the melt in the crucible. It was then cut with a diamond wire saw for further analyses.

X-ray Diffraction. XRD was performed with a D8 Bruker diffractometer operating at the Cu Kα radiation in the Bragg Brentano configuration. The ICSD reference 64709 was used for the indexation of bismuth nanoparticles.

Electron Microscopy. The cut pieces of the samples were observed with SEM (Hitachi SU-70 microscope) equipped with an Oxford X-Max 50 mm 2 EDS detector. For preparation, the samples were mounted in polyfast resin (Struers), followed by a series of polishing steps through 1000, 2400, and 4800 grit papers (Buchler) and then with 3 and 1 μm diamond suspensions. For the observation of nanoparticles in TEM, the nanoparticles were dispersed in ethanol, and then, a drop was evaporated on a carbon-coated copper grid. For the composites, TEM thin films were prepared by a parallelepiped rectangle of 3 × 3 × 0.5 mm cut in the middle of the sample. Polishing was then performed with a diamond disk to get a 200 μm thick sample. The thickness of the center of the slab was further reduced to 50 μm using a precision dimpling instrument (model 515, South Bay Technology). Ion milling was performed for the final thinning. TEM images were recorded on a Tecnai spirit G2 microscope operating at 120 kV. The STEM images and STEM-EDX mapping were recorded with a JEM 2100Plus UHR microscope and a Tecnai F20 that were both operating at 200 kV. The size distribution of precursor nanoparticles was determined by counting 150-200 nanoparticles from TEM images. The size distribution of nanoparticles in the final nanocomposites was determined by counting 70-120 particles, from SEM images and TEM images, respectively. The ICDD 00380471 was used for the indexation of AlSi 2 O 5 .

Chemical Analysis by Wavelength-Dispersive X-ray Fluorescence Spectrometry. WDXRF spectroscopy was performed on the cut pieces of the samples with a Bruker S8 Tiger spectrometer equipped with a Rh tube.

Differential Scanning Calorimetry. DSC analysis was performed in a TA Q20 DSC instrument. Around 10 mg of nanoparticles was sealed in an aluminum crucible. In the case of nanocomposites, top, middle, and bottom slices of the samples (around 40 mg) were analyzed separately to assess the homogeneity of the cast samples. To study the phase transition, the samples were heated to 320 °C and then cooled to 20 °C with a slope of 10 °C min -1 followed by 3 min of stabilization at 20 °C between each cycle. For each sample, at least three cycles were performed.

In Situ TEM. The experiments were carried out using a JEOL JEM-2100F microscope operated at 200 kV equipped with a spherical aberration probe corrector (Cs from CEOS), high-resolution objective lens pole piece, and an UltraScan 1000 CCD array detector (GATAN). For in situ observations, a Protochips Atmosphere system was used. The samples were suspended between two micro-electromechanical system-based closed cells made up of transparent SiN x (for observation) and SiC (for heating). The in situ observations were carried out at a pressure of 1 atm under Ar at a flow rate of 0.1 mL min -1 . The system was previously purged during few hours with the argon environment to ensure the proper cleaning of the gas lines and the sample holder.

Figure 1 .

 1 Figure1. Scheme of the synthesis procedure for aluminum nanocomposites embedding Bi nanoparticles and Bi@SiO 2 core@ shell nanoparticles. Pathways A and B correspond to the ultrasoundassisted incorporation of Bi and Bi@SiO 2 nanoparticles, respectively. Figure 2. (a) TEM image and size distribution of Bi nanoparticles. (b) TEM image and (c) core projected area distribution of Bi@SiO 2 nanostructures. (d) TEM image and size distribution of Bi@SiO 2 nanostructures heated at 350 °C. The white squares are showing areas where coalescence of the cores occurred within silica shells.

Figure 2 .

 2 Figure1. Scheme of the synthesis procedure for aluminum nanocomposites embedding Bi nanoparticles and Bi@SiO 2 core@ shell nanoparticles. Pathways A and B correspond to the ultrasoundassisted incorporation of Bi and Bi@SiO 2 nanoparticles, respectively. Figure 2. (a) TEM image and size distribution of Bi nanoparticles. (b) TEM image and (c) core projected area distribution of Bi@SiO 2 nanostructures. (d) TEM image and size distribution of Bi@SiO 2 nanostructures heated at 350 °C. The white squares are showing areas where coalescence of the cores occurred within silica shells.

Figure 3 .

 3 Figure 3. SEM and TEM study of a Bi@SiO 2 -Al nanocomposite. (a) Backscattered electron imaging-mode SEM image, bright and dark areas correspond to Bi and Al regions, respectively. (b-d) TEM images. Dark and light contrasts correspond to Bi and Al, respectively. Insets in (a,b) show the corresponding size distributions. The inset in (d) shows the SAED pattern corresponding to image (d) and indexed along the structure of bismuth.

Figure 4 .

 4 Figure 4. (a) STEM-HAADF, (b) STEM images, and corresponding (c-f) STEM-EDS maps of Bi@SiO 2 -Al nanocomposites.

Figure 5 .

 5 Figure 5. HRTEM study of the areas highlighted in Figure 4a,b by white, green, and violet squares. (a) HRTEM image acquired in the white rectangle in Figure 4a,b. (b,c) Fast Fourier transforms (FFTs) corresponding to the orange and green squares in (a) and in Figure 4a,b. The FFT in (b) for the Al/O-rich area is indexed along the structure of α-Al 2 O 3 . The FFT in (c) for the Si-rich area shows an amorphous region. (d) HRTEM image shown in the blue rectangle in Figure 4a,b. (e) FFT of the violet area in (d), indexed along the structure of Al 2 SiO 5 .

Figure 6 .

 6 Figure 6. XPS analysis of Bi@SiO 2 -Al nanocomposites (red) and pure Al (black) at the (a) Si 2p and (b) Si 2s areas.

Figure 7 .

 7 Figure 7. (a) SEM image and corresponding size distribution and (b) TEM image of a Bi-Al nanocomposite.

Figure 8 .

 8 Figure 8. DSC analysis of (a) Bi@SiO 2 during the third heatingcooling cycle and of (b) Bi nanoparticles (green). (c, d) DSC analysis of a Bi@SiO 2 -Al nanocomposite with (c) zoomed-in area at 250-290 °C during heating for the 1st and 15th cycles (orange). The DSC analysis of a reference Bi-Al sample is also shown for the first heating ramp in (c) (black).

ACKNOWLEDGMENTS

The authors would like to thank for the funding by Stellantis and ANRT and the framework of the OpenLab βχΦ involving Stellantis and Sorbonne University. We thank the Institut des Matériaux de Paris Centre (IMPC FR2482) for servicing FEG-SEM, EDX and XPS measurements (David Montero, Antoine Miche), Laboratoire de Réactivité de Surface (UMR 7197) for servicing TEM (Patricia Beaunier), and Sorbonne Université, CNRS, and C'Nano projects of the Region Ile-de-France for funding.

AUTHOR INFORMATION

Supporting information

Liquid Processing of Bismuth-Silica Nanoparticle/Aluminum Matrix Nanocomposites for Heat Storage Applications

Binghua Ma, [START_REF] Wu | Heat Dissipation Design for Lithium-Ion Batteries[END_REF][START_REF] Qu | Numerical Model of the Passive Thermal Management System for High-Power Lithium Ion Battery by Using Porous Metal Foam Saturated with Phase Change Material[END_REF] Walid Baaziz, [START_REF] Alipanah | Numerical Studies of Lithium-Ion Battery Thermal Management Systems Using Phase Change Materials and Metal Foams[END_REF] Léo Mazerolles, [START_REF] Wu | An Experimental Study of Thermal Management System Using Copper Mesh-Enhanced Composite Phase Change Materials for Power Battery Pack[END_REF] Ovidiu Ersen, [START_REF] Alipanah | Numerical Studies of Lithium-Ion Battery Thermal Management Systems Using Phase Change Materials and Metal Foams[END_REF] Bernard Sahut, [START_REF] Qu | Numerical Model of the Passive Thermal Management System for High-Power Lithium Ion Battery by Using Porous Metal Foam Saturated with Phase Change Material[END_REF] Clément Sanchez, [START_REF] Wu | Heat Dissipation Design for Lithium-Ion Batteries[END_REF] david.portehault@sorbonne-universite.fr stephane.delalande@stellantis.com