domain: sde chim sdu

text:
Camille Thomas
email: camille.thomas@unige.ch

Vincent Grossi
email: vincent.grossi@univ-lyon1.fr

Ingrid C Antheaume

Daniel Ariztegui

Recycling of archaeal biomass as a new strategy for extreme life in Dead Sea deep sediments

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

INTRODUCTION

In extreme environments, microbial metabolic processes that lower the energetic cost of maintaining life are favored [START_REF] Hoehler | Microbial life under extreme energy limitation[END_REF]. Such settings are characterized by low growth rates [START_REF] Lomstein | Endospore abundance, microbial growth and necromass turnover in deep sub-seafloor sediment[END_REF], and most energy is diverted to maintenance functions [START_REF] Van Bodegom | Microbial maintenance: A critical review on its quantification[END_REF], such as osmotic equilibration, oxygen stress defense, motility, and more sustainable metabolic pathways. These selective conditions for life promote the dominance of prokaryotes and generally favor Archaea relative to Bacteria [START_REF] Hoehler | Microbial life under extreme energy limitation[END_REF]. This is mostly due to the reduced membrane permeability of Archaea, which requires less maintenance energy with respect to bacterial membranes [START_REF] Valentine | Adaptations to energy stress dictate the ecology and evolution of the Archaea[END_REF]. This advantage is particularly striking in environments characterized by high osmotic stress such as hypersaline environments. There, bacteria may use alternative strategies that allow competition with the putatively better-adapted archaea, for example, by recycling available organic molecules as osmotic solutes (Oren, 1999a). The intracellular accumulation of ambient organic carbon is a common way of economizing energy in harsh environments. For example, under stressed growth conditions, some Bacteria species are known to accumulate intracellular lipid droplets [START_REF] Alvarez | Lipid storage compounds in marine bacteria[END_REF][START_REF] Wältermann | Neutral lipid bodies in prokaryotes: Recent insights into structure, formation, and relationship to eukaryotic lipid depots[END_REF] in the form of polyhydroxyalkanoates, triglycerides, or wax esters (WEs). However, the presence of such mechanisms in the deep biosphere has not yet been documented, suggesting that they may not be sufficiently effective in these low-energy settings for bacteria to survive.

The Dead Sea is the most saline lake on Earth and has deposited evaporitic minerals since the early Quaternary [START_REF] Stein | The sedimentary and geochemical record of Neogene-Quaternary water bodies in the Dead Sea Basin-Inferences for the regional paleoclimatic history[END_REF]. As a result, its subsurface environment constitutes one of the most extreme ecosystems on the planet. The extreme chemistry of the water allows only for the survival of halophilic archaea in the water column and recent halite sediments [START_REF] Bodaker | Comparative community genomics in the Dead Sea: An increasingly extreme environment[END_REF][START_REF] Thomas | Impact of paleoclimate on the distribution of microbial communities in the subsurface sediment of the Dead Sea[END_REF]. Bacteria have rarely been observed in the most extreme sedimentary facies of the Dead Sea (halite or gypsum), suggesting that these harsh conditions limit their growth [START_REF] Thomas | Impact of paleoclimate on the distribution of microbial communities in the subsurface sediment of the Dead Sea[END_REF]. To further investigate the composition of the microbial community and its potential metabolic strategies to survive the most arid periods of the late Quaternary of the Dead Sea basin, we characterized the lipid biomarker composition of deep hypersaline halite and gypsum sediments. The good lipid preservation in these extreme horizons gives clues to metabolic pathways that allow for the survival of a thus-farunrecognized deep biosphere.

METHODS

The material used in this study originated from the International Continental Scientific Drilling Program (ICDP) Dead Sea Deep Drilling Project (DSDDP) core 5017-1A retrieved from the center of the Dead Sea in winter 2010-2011 (Fig. 1). Sediments were sampled on site from core catchers using sterile tools and kept in the freezer at -20 °C until further processing. The main characteristics of samples analyzed for lipid biomarkers are given in Table DR1 in the GSA Data Repository 1 . Samples were freeze-dried, ground, and extracted using multiple sonication cycles (methanol 2×, methanol/dichloromethane [DCM] [1:1 v/v, 2×], and DCM 3×). Elemental sulfur was removed with activated copper. Lipid extracts were filtered and separated using a deactivated silica gel column (5% H 2 O) into five fractions of increasing polarity (hexane/DCM [9:1 v/v], hexane/DCM [1:1 v/v], DCM, ethyl acetate, and methanol). Fractions 3 and 4 were silylated with pyridine/ bis(trimethylsilyl) trifluoroacetamide (BSTFA) at 2:1 (v/v). Fraction 5 was trans-esterified by overnight incubation with 0.5 mL of toluene and 2 mL of 2% H 2 SO 4 in methanol at 60 °C. Following the addition of NaCl (5%), the organic phase was extracted with hexane:DCM (4:1 v/v, 3×), washed with NaHCO 3 (2%), and dried with sodium sulfate before silylation.

All fractions were analyzed by gas chromatography mass spectrometry (GC-MS) on an HP 6890 Series Plus gas chromatograph equipped Compound-specific carbon isotope (δ 13 C) analyses were done using a HP7890B GC coupled to an Isoprime visION isotope ratio mass spectrometer via a GC-5 combustion interface operating at 870 °C. The GC was equipped with a BPX5 column (30 m × 0.25 mm × 0.10 µm, SGE Analytical Science) and a cool on-column injector, and the oven temperature was programmed as for GC-MS analyses. The B4 standard mixture (Arndt Schimmelmann, Indiana University, USA) was used to externally calibrate the δ 13 C values, and the known BSTFA derivatizing agent δ 13 C value was corrected for alcohols and fatty acids. After samples were decalcified, total organic carbon (TOC) was measured, and data were normalized to a standard sediment (IVA Analysentechnik, Germany) containing 9.15% of carbon, measured every 10 samples.

RESULTS AND DISCUSSION

We found significant amounts of isoprenoid wax esters (WEs; up to 0.2 µg g -1 TOC) in halite and gypsum samples retrieved between 90 and 250 m below lake floor (mblf; Fig. 2; Table DR1). WEs are an important type of energy-storage molecule that can be produced by Eukarya and Bacteria, particularly under conditions of stress, but they have not been reported in the archaeal domain so far [START_REF] Wang | Bioinformatics analysis of metabolism pathways of archaeal energy reserves[END_REF]. WEs are formed by condensation of fatty acids and alcohols available in the environment and provide easily accessible (intracellular) sources of carbon [START_REF] Wältermann | Neutral lipid bodies in prokaryotes: Recent insights into structure, formation, and relationship to eukaryotic lipid depots[END_REF]. The major WE detected was phytanyl phytanate (3,7,11,15-tetramethylhexadecyl-3,7,11,15-tetramethylhexadecanoate; iC 20 -iC 20 ; Fig. 3), which was accompanied by a series of other isoprenoid WEs composed of a C 20 or C 25 isoprenoid alcohol esterified to a linear, methyl-branched or isoprenoid acyl chain (Fig. 3A). The isoprenoid WEs were accompanied by significant amounts of membrane core lipids of halophilic archaea-archaeol (2,3-O-diphytanyl-sn-glycerol) and extended archaeol (2,3-O-phytanyl-O-sesterterpanyl-snglycerol; [START_REF] Kates | Diether and tetraether phospholipids and glycolipids as molecular markers for Archaebacteria (Archaea)[END_REF][START_REF] Dawson | Molecular characterization of core lipids from halophilic archaea grown under different salinity conditions[END_REF]along with several of their hydrolyzed and eventually oxidized products (Figs. 2 and3B; Table DR2). Lipids of halophilic archaea are composed of C 20 and C 25 isoprenoid alkyl chains, which allow for better control of membrane permeability under strong osmotic stress compared to bacterial fatty acid membranes [START_REF] Valentine | Adaptations to energy stress dictate the ecology and evolution of the Archaea[END_REF][START_REF] Koga | Thermal adaptation of the archaeal and bacterial lipid membranes: Archaea[END_REF]. Archaeol was the most abundant isoprenoid alcohol in most of the investigated sediment samples (Figs. 2 and3). Extended archaeol, a membrane lipid more specific to halophilic archaea of the Halobacteria class [START_REF] Dawson | Molecular characterization of core lipids from halophilic archaea grown under different salinity conditions[END_REF]) that dominate the Dead Sea halite and gypsum sediments [START_REF] Thomas | Impact of paleoclimate on the distribution of microbial communities in the subsurface sediment of the Dead Sea[END_REF], was also found to be enriched in halite and gypsum facies. In most of the sedimentary intervals where WEs occurred, archaeol and extended archaeol were found in lower abundance than in the other halite/gypsum samples (Fig. 2). All WE-containing intervals also contained hydrolyzed core lipids and some oxidized counterparts (Fig. 2), which consisted of C 20 and C 25 isoprenoid alcohols and acids (phytanol, phytanic acid, and C 25 homologues), and of isoprenoid C 20 and C 25 monoalkyl glycerol monoethers (Fig. 3B; Table DR2). The report of WEs with isoprenoid C 25 carbon chains (iC 20 -iC 25 and iC 25 -iC 20 ) in environmental samples is unprecedented and attests to the incorporation of archaeal core lipid subunits into isoprenoid WEs. The formation of isoprenoid WEs has been previously demonstrated during growth of bacteria on free isoprenoid compounds such as phytane, phytol, or squalene [START_REF] Rontani | Biodegradation of free phytol by bacterial communities isolated from marine sediments under aerobic and denitrifying conditions[END_REF][START_REF] Rontani | Production of a polyunsaturated isoprenoid wax ester during aerobic metabolism of squalene by Marinobacter squalenivorans sp[END_REF][START_REF] Silva | Biodegradation of phytane (2,6,10,14-tetramethylhexadecane) and accumulation of related isoprenoid wax esters by Mycobacterium ratisbonense strain SD4 under nitrogen-starved conditions[END_REF]. The recycling of phytol derivatives into mixed isoprenoid and non-isoprenoid WEs has also been reported in desiccated mats from modern evaporative alkaline lakes [START_REF] Finkelstein | Microbial biosynthesis of wax esters during desiccation: Adaptation for colonization of the earliest terrestrial environments?[END_REF]. However, phytane, phytol, and squalene were not observed in the investigated samples from the Dead Sea. As a result, the WEs detected in the Dead Sea halite/gypsum samples mostly consist of lipid subunits derived from the cell walls of archaea, the dominant organisms in the Dead Sea [START_REF] Bodaker | Comparative community genomics in the Dead Sea: An increasingly extreme environment[END_REF][START_REF] Thomas | Impact of paleoclimate on the distribution of microbial communities in the subsurface sediment of the Dead Sea[END_REF]. This recycling was further confirmed by compound-specific stable carbon isotope analyses (Fig. DR1), showing that the δ 13 C values of WEs agree with those of their building blocks (fatty acids and alcohols). Current knowledge suggests that the ability to form and accumulate WEs has only arisen in the Bacteria and Eukarya Domains [START_REF] Garay | Accumulation of high-value lipids in singlecell microorganisms: A mechanistic approach and future perspectives[END_REF][START_REF] Wang | Bioinformatics analysis of metabolism pathways of archaeal energy reserves[END_REF]. Eukaryotic life in the Dead Sea has been constrained to humid intervals during glacial periods and occasional blooms triggered by high precipitation during interglacial stages [START_REF] Oren | A bloom of Dunaliella parva in the Dead Sea in 1992: Biological and biogeochemical aspects[END_REF]. Hence, isoprenoid WEs present in the most arid intervals of the Dead Sea sediment (halite/gypsum) cannot have originated from eukaryotes. So far, archaea have never been shown to produce WEs. Given the prevalence of archaea in the Dead Sea environment, and the fact that ubiquitous groups of archaea have been shown to possess the genetic machinery for fatty acid synthesis [START_REF] Iverson | Untangling genomes from metagenomes: Revealing an uncultured class of marine euryarchaeota[END_REF][START_REF] Villanueva | Phylogenomic analysis of lipid biosynthetic genes of Archaea shed light on the 'lipid divide[END_REF], an archaeal origin of the iso prenoid WEs cannot be completely ruled out. However, recent bioinformatics analysis of presently available archaeal genomes (including some hyperhalophiles) has failed to identify homologues of genes coding for a wax ester synthase [START_REF] Wang | Bioinformatics analysis of metabolism pathways of archaeal energy reserves[END_REF]. Abiotic esterification has also been suggested to occur during the transformation of sedimentary lipids [START_REF] Becker | Biogeochemical Significance and Biomarker Potential of Novel Glycerolipids and Respiratory Quinones in the Marine Environment[END_REF]. However, the activity of extracellular enzymes is likely to be inhibited in the hypersaline Dead Sea sediment [START_REF] Frankenberger | Influence of salinity on soil enzyme activities[END_REF][START_REF] Grant | Life at low water activity: Philosophical Transactions of the Royal Society of London[END_REF], and, in the present case, putative abiotic reactions would have yielded a much wider diversity of ester structures (including eukaryotic sterols). The predominance of specific bacterial C 15 and C 17 fatty acids [START_REF] Perry | Fatty acids of bacterial origin in contemporary marine sediments[END_REF] in WEcontaining sedimentary intervals (Fig. 2) instead suggests a bacterial origin of the WEs. Recent work on fluid inclusions allowed the retrieval of bacterial sequences in halite-dominated layers of the deep Dead Sea sediment [START_REF] Thomas | Fluid inclusions from the deep Dead Sea sediment provide new insights on Holocene extreme microbial life: Quaternary Science Reviews[END_REF]. Our data would therefore support the development of bacteria upon archaeal necromass in the extreme environment of the Dead Sea subsurface.

The concentration of intracellular WEs in bacteria has been shown to arise particularly in situations of nutrient starvation, especially when nitrogen is limiting [START_REF] Ishige | Wax ester production by bacteria[END_REF]. Additionally, the recycling of archaeal biomass by archaea themselves was suggested to minimize the energy cost of life in nutrient-and carbonlimited environments [START_REF] Takano | Sedimentary membrane lipids recycled by deep-sea benthic archaea[END_REF]. Here, we propose that the recycling of archaeal necromass into storage lipids like WEs constitutes a way to save and store energy in the nutrient-and energy-demanding hypersaline environment of the Dead Sea. The presence of WEs with mixed linear and isoprenoid acyl chains alongside the predominant isoprenoid WEs indicates that the WE-forming microbial population also recycled bacterial and/or eukaryal fatty acids, in addition to archaeal lipids (Fig. 4). This evidence suggests that the subsurface biosphere creates easily accessible carbon stocks from necromass, in a lake with rare allochthonous inputs and primary production (Oren, 1999b).

The accumulation of WEs has previously been reported in episodically desiccated microbial mats in hypersaline environments [START_REF] Finkelstein | Microbial biosynthesis of wax esters during desiccation: Adaptation for colonization of the earliest terrestrial environments?[END_REF], and this was suggested to allow for better microbial cell survival during periods of desiccation, due to the release of H 2 O during esterification. The Dead Sea sediments that bear WEs originate from the deepest part of the lake (i.e., 297.5 m below lake level), where neither traces of desiccation [START_REF] Neugebauer | Hydroclimatic variability in the Levant during the early last glacial (~117-75 ka) derived from micro-facies analyses of deep Dead Sea sediments: Climate of the Past[END_REF] nor microbial mats have been reported. These sedimentary levels correspond, however, to periods when salinity was the highest in the deep brine, as supported by the bromide concentration curve (Fig. 2), a reliable salinity proxy for the Dead Sea water column during the Quaternary [START_REF] Levy | Pore fluids in Dead Sea sediment core reveal linear response of lake chemistry to global climate changes[END_REF]. The WE-bearing levels generally correspond to the highest concentrations of this element (Fig. 2; Table DR1), which are observed during the driest climatic intervals (marine isotopic stages 5E, early 5A, and the 2/1 transition). Water availability is a major issue, not only in dry environments, but also in hypersaline settings due to high salt concentrations inducing osmotic stress. By recombining hydrolyzed moieties of core lipids originating from the buried archaeal necromass, the WE-forming bacterial population may therefore have created accessible water molecules that favored its survival.

Our results illustrate the high adaptability of the subsurface biosphere and its ability to use varied strategies for energy production and preservation under adverse conditions. By studying an environment that pushes life to its limits, we catch a glimpse of the processes that fuel life in the deep subsurface, and we advance our understanding of the deep sedimentary carbon cycle.

  www.gsapubs.org | Volume 47 | Number 5 | GEOLOGY | Geological Society of America with a cool on-column injector, and coupled to an Agilent 5975C (VL MSD) mass spectrometer. The GC was equipped with an HP5 column (30 m × 0.25 mm × 0.25 µm, RESTEK). Samples were injected at 60 °C (held for 30 s), and the oven temperature was increased to 130 °C at a rate of 20 °C/min, then to 250 °C (5 °C/min) and 300 °C (3 °C/min), and finally held isothermal for 45 min. Stepwise dilution of external standards allowed quantification. Isoprenoid fatty acids are less polar than linear acids and partly eluted in fraction F4. A ratio based on specific bacterial fatty acids was calculated using the sum of methyl-branched C 15 and C 17 fatty acids over the sum of linear C 15 and C 17 fatty acids. Changes in this ratio indicate a shift in bacterial community.

  Figure 1. Localization of Dead Sea cores. Material used in this study originated from International Continental Scientific Drilling Program (ICDP) Dead Sea Deep Drilling Project (DSDDP) drilling site 5017-1.

Figure 2 .

 2 Figure 2. Occurrence of wax esters (WEs) and other lipid biomarkers in Dead Sea core 5017-1 (gypsum and halite intervals are shown in black; dashed lines indicate position of samples analyzed). "Hydrolyzed core lipids" refer to degradation products of archaeol and extended archaeol, which are presented as alkyl chains. TOC-total organic carbon; mblf-meters below lake floor. Bacterial fatty acids ratio (sum of methyl-branched C 15 and C 17 over sum of linear C 15 and C 17 fatty acids) indicates presence of bacteria potentially involved in WE production. Sampled layers are compared to bromide concentration in sediment pore waters (used as a proxy for lake-water dilution/concentration; Levy et al., 2017) and fitted to 14 C ages (Kitagawa et al., 2016) and U/Th ages (Torfstein et al., 2015). Formation of WE building blocks is schematized on righthand side of figure. Red items indicate an archaeal origin; blue items indicate a bacterial origin.

  Figure 4. Schematic pathway for isoprenoid wax ester (WE) formation by recycling of membrane lipids in Dead Sea sediments. Intact phospholipids from archaeal and bacterial/eukaryal communities are hydrolyz ed and eventually oxidized, and some of these degradation products are transformed into WEs by bacterial communities in deep halite/gypsum sediment horizons. Symbols refer to compounds identified in Figure 3. Red moieties have an archaeal origin; blue moieties have a bacterial/eukaryal origin.

Figure 3 .

 3 Figure 3. Characteristic chromatograms of isoprenoid wax esters (WEs) and related building blocks. A: Total ion chromatogram (TIC) of WE fraction (fraction 2) from gypsum sample at 91.04 m below lake floor (mblf). B: Extracted ion chromatogram (m/z 103+133+159+205+218) of a polar lipid fraction (fraction 4) from gypsum sample at 243.22 mblf. These intervals were selected because they show highest concentrations of isoprenoid WEs and hydrolyzed core lipids, respectively. MGM-monoalkyl glycerol monoethers. Red symbols indicate an archaeal origin; blue symbols indicate a bacterial/eukaryal origin. Numbers above symbols shown in B correspond to number of carbon atoms in chain.

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ACKNOWLEDGMENTS

We thank the scientific and technical teams of the Dead Sea Deep Drilling Project (DSDDP, www .icdp -online .org), and A. Vinçon-Laugier, A. Vuillemin, and M. Makou. Sponsorship from the International Continental Scientific Drilling Program and funding by the Swiss National Science foundation (projects 200021-132529 and 200020-149221/1 to Ariztegui) and the French National Research Agency (grant ANR-12-BSV7-0003 to Grossi) are acknowledged. We are grateful to Yuki Weber and an anonymous reviewer for their very constructive comments on an earlier version of the manuscript.


Hamidou Dembele
email: hamidou.dembele@orange.com

Marie Le Bot

Franc ¸ois GALLEE † Patrice Pajusco

Impact of Human Blockage on 5G Communication System in the 26 GHz Band

Keywords: Human blockage, mmWaves, 26 GHz, propagation, 5G, BLER, link budget, antenna

This paper deals with the impact of human blockage on 5G communication systems in millimeter-wave bands. The analysis of communication performance in terms of block error rate with and without the influence of human blockage is proposed in the 26 GHz band, thanks to the implementation of the 5G physical layer in a simulation tool. Based on the block error rate performance, a link budget is also established in order to evaluate the impact of blockage on the cell coverage.

I. INTRODUCTION

The 5G New Radio (NR) is standardized by the 3rd Generation Partnership Project (3GPP) with several changes in the physical (PHY) layer processing. Regarding the channel, a 3D model based on clustered delay line (CDL) is designed including antenna radiation pattern defined in elevation and azimuth for the link-level evaluations. This channel model is proposed for carrier frequencies extended to millimeter wave (mmWave) bands up to 100 GHz.

The NR communication chain at the transmitter is proposed for diversifying 5G major use-cases, among which the enhanced mobile broadband (eMBB) applications, that requires high data rates. In this context, the use of mmWave bands takes advantage of the enormous amount of spectrum available in this region of frequencies.

Although these frequencies offer opportunities in terms of huge bandwidth for wireless communication, they are victims of severe attenuation with high sensitivity to blockage leading to communication failure due to the presence of human or vehicle in the propagation environment.

In the literature, several theoretical and experimental approaches are proposed for modelling human blockage in mmWave bands for 5G communication. Nonetheless, the consequence of the attenuation due to the presence of human or vehicle on the 5G communication system is rarely investigated. In [2], Momo and Mowla analyse the impact of human blockage on the channel statistics such as root mean square (RMS) delay spread and pathloss in the 28, 38, 60 and 73 GHz bands. Moltchanov et al. [START_REF] Moltchanov | Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment[END_REF] justify that the presence of human blockers between the user and the base station (BS) leads a serious drop in the throughput at the 5G NR air interface at 28 GHz. To our knowledge, so far there are no works reported in the open literature on the impact of human attenuation on the end-to-end 5G PHY layer communication performance in terms of block error rate (BLER).

The first objective of this paper is to evaluate the BLER performance of the entire 5G physical layer chain in the 26 GHz mmWave band using sector antenna. And then the influence of human blockage on these results is highlighted. The second objective is to establish from the resulting BLER, the link budget for the cell coverage with and without blockage.

The rest of the paper is organized as follows. Section II briefly describes the 5G NR communication system in downlink. In section III, the propagation channel model used in this work is presented. Section IV focuses on the model of human blockage implemented in the communication chain. Section V is dedicated to simulation results. And finally, conclusion and future studies are given in section VI.

II. 5G COMMUNICATION SYSTEM MODEL

The goal of this section is to give an overview of the 5G downlink transmission chain implemented as part of this study. The end-to-end data processing is highlighted in Fig. 1, including both transmitter and receiver chains.

A. 5G PHY Transmission Chain

Several processing blocks are defined in the 3GPP specification [START_REF] Gpp | Technical Specification Group Radio Access Network; NR; Multiplexing and channel coding[END_REF] and [START_REF] Gpp | Technical Specification Group Radio Access Network; NR; Physical channels and modulation[END_REF] for the 5G NR data transmission. Fig. 1 shows that after the generation of a transport block, a sequence of cyclic redundancy check (CRC) bits is computed and attached to this one before low-density parity check (LDPC) channel encoding and the rate matching processing. The goal of the rate matching block in the communication chain is to dynamically adapt the number of bits on LDPC encoder output to the amount of bits expected for a given modulation and coding scheme (MCS). Multiple-input multiple-output (MIMO) precoding is applied here in order to adapt the number of spatial layers (streams) to the number of antennas active for the transmission. Before the transmission of data, orthogonal frequency-division multiplex (OFDM) numerology is used.

B. PHY Processing at the Receiver

After receiving data symbols, the first key signal processing here is the equalization operation whose purpose is to compensate different distortions or fading incurred by the signal through the transmission channel. In this communication chain, minimum mean square error (MMSE) equalizer is implemented. Furthermore, another key processing is the soft demodulation used to convert the equalized data symbols to a stream of values needed for LDPC decoding algorithms. Decoded bits from LDPC decoder are delivered for BLER computation after code block desegmentation and CRC detachment which represent the reverse functionalities of code block segmentation and CRC attachment at the transmitter, respectively. To determine the performance of the entire communication chain, the BLER is calculated by comparing the initial transport block with that received.

III. CDL CHANNEL MODEL

For the multipath propagation model regarding the communication system, clustered delay line (CDL) channel models in [START_REF] Gpp | Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz[END_REF] are adopted. CDL models were designed for 3D propagation characterized by departure and arrival angles in elevation and azimuth as illustrated in Fig. 2 Their expressions are defined hereby [START_REF] Gpp | Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz[END_REF]:

φ n,m,AoD = φ n,AoD + c ASD α m , (1) φ n,m,AoA = φ n,AoA + c ASA α m , (2) θ n,m,ZoD = θ n,ZoD + c ZSD α m , (3) θ n,m,ZoA = θ n,ZoA + c ZSA α m , (4)
where c ASD , c ASA , c ZSD and c ZSA represent azimuth spread of departure angle, azimuth spread of arrival angle, elevation spread of departure angle and elevation spread of arrival angle, respectively, for the selected profile. φ n,AoD , θ n,ZoD , φ n,AoA I summarizes CDL E profile parameters used as channel model in the context of this study. In CDL channel model, the principle of delay scaling factor DS desired is introduced from which the propagation delay τ n of cluster n is defined by:

τ n = τ n,model • DS desired , (5)
with τ n,model the normalized delay of cluster n whose values are mentioned for each CDL profile, namely CDL E.

IV. 3GPP BLOCKAGE MODEL

As mentioned in the introduction of the paper, realistic experimental results have been published in the literature on human attenuation values in mmWave bands. Some models are based on the diffraction theory from which a computational formula is derived to predict the blockage attenuation. However, in this work about the 5G communication, we mainly focused on the modelling standardized by the 3GPP.

A. General Description

Blockage constitutes one of mmWave propagation channel features, which is characterized by severe attenuation of signal strength caused by pedestrians or vehicles. Two types of blockages are identified, one is called self-blocking, which means that the radio link is blocked by the user itself most often by its hand. The other type called non-self-blocking is due to the presence of humans or vehicles moving around the user. Both types are presented in Fig. 3. Note that the presence of blockers in this context of study does not change the channel state, i.e the LOS state cannot change in NLOS even if the direct path is blocked. The idea of blocking a cluster is just to attenuate the channel gain of this cluster as detailed in the rest of the section.

B. Blockage Modelling

In the 3GPP specification, two models (A and B) of blockage are proposed, each with a specific use case. Model A is used in computational environment and model B is desired when a realistic environment is adopted. In our study, we only focused on the approach given in model A. As illustrated in Fig. 4, model A is a stochastic model in which the blocker is modelled by a blocking region at the receiver. The principle Fig. 4: Blocking scenario. of this approach is to attenuate any cluster whose arrival angle falls into the blocking region. The blocking region is characterized by an angular spread in elevation defined from θ c -∆θ to θ c + ∆θ and an angular spread in azimuth defined from φ c -∆φ to φ c + ∆φ. ∆θ and ∆φ represent angular spread with high attenuation centred around θ c and φ c in elevation and azimuth, respectively. It is clear that a cluster is blocked if its arrival angles in elevation θ ZoA and azimuth φ AoA are such that θ ZoA ∈ {θ c -∆θ, θ c + ∆θ} and φ AoA ∈ {φ c -∆φ, φ c + ∆φ}, and the channel gain associated to this cluster is attenuated in dB units by [START_REF] Gpp | Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz[END_REF]:

L dB = -20log 10 (1 -(F (a 1 ) + F (a 2 )) • (F (z 1 ) + F (z 2 ))) , (6) where a 1 = φ AoA -φ c + ∆φ 2 , a 2 = φ AoA -φ c -∆φ 2 , z 1 = θ AoA -θ c + ∆θ 2
and z 2 = θ AoA -θ c -∆θ 2 and F (x) is defined for x ∈ {a 1 , a 2 , z 1 , z 2 } by:

F (x) = s (x) 1 π atan π 2 π λ 0 r 1 cos (x) -1 , (7)
with

s (x) = +1 if x ≤ 0 -1 if x > 0 , x ∈ {a 1 , z 1 } , (8)
or

s (x) = +1 if x > 0 -1 if x ≤ 0 , x ∈ {a 2 , z 2 } . ( 9
)
And r in ( 7) represents the distance from the blocker to the receiver (Rx).

V. SIMULATION RESULTS

This section is dedicated to the presentation and analysis of simulation results regarding the scenarios of interest as well as the study assumptions. The whole 5G physical layer including the multipath channel with the blockage phenomenon, has been developed in C++ based simulation tool. Only one base station (BS) considered as the transmitter and one user equipment (UE) considered as the receiver are used. The communication chain given in Fig. 1 is used for link-level evaluation. Both scenarios with and without human blockage are considered in indoor environment. The blocking system is applied such that the LOS path fully meets the center of the blocker whose parameters are given in Table II. We assume that the blockage phenomenon occurs during the whole data transmission between the BS and the UE. The same antenna A. Attenuation from human blockage at 26 GHz

We first start by analyzing the attenuation by human body in the 26 GHz band using the 3GPP blockage model presented in section IV-B. Fig. 5: Human blockage attenuation in dB units. Fig. 5 shows this attenuation in dB units with respect to azimuth φ and elevation θ angles in the blocking region with parameters given in Table II. From this figure, we notice that the attenuation is all the more significant as the direction of propagation path or cluster is closer to the center of the blocker characterized here by angle in azimuth φ c = -180 • and angle in elevation θ c = 80.4 • . This mechanism is justified by the modelling of human blocker through a rectangular screen for which a diffraction loss given in ( 7) is applied. The diffraction approach highlights the fact that if the cluster is closer to the knife-edges of the screen, the signal power is very lowly attenuated. And if the cluster meets the center of the screen, the signal strength is attenuated to the maximum. Note that due to cosine expression in [START_REF] Zhao | Attenuation by Human Bodies at 26-and 39.5-GHz Millimeter Wavebands[END_REF], the value of attenuation admits a symmetric axis in each of both plans, azimuth and elevation through φ c and θ c , respectively. Therefore, the attenuation ranges from 0 to 12.71 dB at 26 GHz for one blocker. These values are justified by measurements conducted by Zhao et al. [START_REF] Zhao | Attenuation by Human Bodies at 26-and 39.5-GHz Millimeter Wavebands[END_REF] in indoor environment to quantify the value of human body attenuation at 26 GHz. Results show that the communication link is attenuated by a maximum value of 12.66 dB for the presence of one person between the Tx and the Rx. Nonetheless, it is obvious that if the number of human blockers increases, the resulting attenuation also increases.

B. Link Level Evaluation

The actual 5G NR is performed here in order to show that the impact of human blockage can be observed on communication performance in terms of BLER. For the link level evaluation, a single-input single output (SISO) scenario is chosen. As previously mentioned, the advantage of mmWave is focused on 5G eMBB applications for which the target BLER is 10% [START_REF] Chu | A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks[END_REF]. Fig. 6 highlights the BLER vs SNR performance for both scenarios with and without blockage using MCS 9 (16QAM, code rate = 0.6) and CDL E channel model with one antenna at the transmitter and one antenna at the receiver. The shape of the curves is typical of scenarios with LDPC channel coding. For BLER=10%, we observe that the link without blocker is better than the link including human blocker with a signal-to-noise ratio (SNR) difference about 13 dB. This result is justified by the blockage (attenuation of 12.71 dB) of the LOS path which is the most dominant path. The same trend is observed by comparing the channel power of CDL E profile without blockage with this one of CDL E including the blockage of the LOS cluster.

From the performance BLER=10% given in Fig. 6, we establish the link budget in order to illustrate the consequence of human blockage phenomenon on the system capacity in terms of cell coverage in mobile communication as given in Table III. To this end, P L max represents the maximum value of path loss to determine the cell range in both scenarios with and without the presence of human blockage on the communication link. In the calculation, we consider a transmit power of 10 dBm [START_REF] Yu | Phased Arrays and Architecture Selection[END_REF] and a coverage probability of 95%.

Based on this cell coverage probability and by assuming that Gaussian shadowing is considered with standard deviation of 3 dB, a shadowing margin of 2.95 dB is deduced. As defined in [START_REF] Gpp | Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz[END_REF], the path loss in LOS state propagation environment is defined by: P L LOS (dB) = 32.4+17.3log 10 (d 3D )+20log 10 (f c ) , [START_REF] Nadeem | Elevation Beamforming with Full Dimension MIMO Architectures in 5G Systems: A Tutorial[END_REF] where From this analysis, it can be seen that to guarantee the same quality of service, the communication range must be drastically reduced in the 26 GHz mmWave band.

d 3D = d 2 2D + (h BS -h U E ) 2 ,

VI. CONCLUSION

In this paper, the problem of human blockage that occurs in 26 GHz band for 5G communication system is addressed. To this end, we analyzed in a simulation tool the 5G physical layer, the multipath channel and the blockage model. Simulation results revealed that for the same 5G eMBB quality of service, the 26 GHz communication system with the presence of human in the LOS cluster requires a 13 dB increase in SNR compared to the similar scenario without blockage. For future work, we will extend the analysis to antenna pattern diversity in the context of multi-antenna systems as resolution of communication link blockage by a human. This paper's copyright is held by the author(s). It is published in these proceedings and included in any archive such as IEEE Xplore under the license granted by the "Agreement Granting EurAAP Rights Related to Publication of Scholarly Work."

Fig. 1 :

 1 Fig. 1: Communication block diagram of 5G NR.

  with φ n,m,AoD , θ n,m,ZoD , φ n,m,AoA and θ n,m,ZoA representing azimuth angle of departure, elevation angle of departure, azimuth angle of arrival and elevation angle of arrival of ray m within cluster n, respectively.

Fig. 2 :

 2 Fig. 2: Clustered multipath propagation.

  and θ n,ZoA are azimuth angle of departure, elevation angle of departure, azimuth angle of arrival and elevation angle of arrival of cluster n, respectively. α m defines the offset of ray m angle within the cluster whose values are given in the specification. Five different CDL profiles are proposed as follow. Three profiles for non-line of sight (NLOS) state propagation which are CDL A, CDL B and CDL C. For line of sight (LOS) environment, two profiles CDL D and CDL E are used. Table

Fig. 3 :

 3 Fig. 3: Blocking scenario.

  Blocking region parameters ∆φ = 45 • , φc = -180 • , ∆θ = 15 • , θc = 80.4 • , r = 2 m Number of Normalized Min-Sum (NMS) with normalization factor α = 0.7 LDPC decoding iterations 50 model is used for transmission and reception, and its boresight almost matches the LOS path of the CDL E profile.

Fig. 6 :

 6 Fig. 6: BLER vs SNR in SISO.

Fig. 7 :

 7 Fig. 7: Illustration of d 3D , d 2D , h BS and h U E .

  with d 2D the distance from the BS to the UE which defines the cell range, h BS and h U E the BS and UE heights, respectively, and f c is the carrier frequency. With the maximum path loss P L max from the link budget, for h BS = 3m and h U E = 1.5m in the 26 GHz band, we deduce from (10) that the scenario without human presence on the LOS link has a cell range of d 2Dmax = 316.3 m while the blockage of the LOS link results in a cell range of d 2Dmax = 53.13 m.

TABLE I :

 I CDL E profile parameters.

TABLE II :

 II Simulation Parameters.

	Parameters	Values
	Carrier frequency	26 GHz
	Bandwidth	400 MHz [15]
	Sub-carrier spacing	120 kHz [15]
	FFT size	4096
	Channel model	3GPP LOS Channel Profile CDL E, DS

desired = 100 ns

[START_REF] Gpp | Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz[END_REF]

TABLE III :

 III Link Budget in 26 GHz band.

This paper's copyright is held by the author(s). It is published in these proceedings and included in any archive such as IEEE Xplore under the license granted by the "Agreement Granting EurAAP Rights Related to Publication of Scholarly Work."Authorized licensed use limited to: IMT ATLANTIQUE. Downloaded on August 31,2021 at 13:51:41 UTC from IEEE Xplore. Restrictions apply.

Authorized licensed use limited to: IMT ATLANTIQUE. Downloaded on August 31,2021 at 13:51:41 UTC from IEEE Xplore. Restrictions apply.


Simona Dalla

Erwan Paineau

Jean-Blaise Brubach

Patrick Judeinstein

Stéphan Rouzière

Pascale Launois

Pascale Roy

Water in carbon nanotubes: the peculiar hydrogen bond network revealed by infrared spectroscopy

Keywords: Carbon nanotube, confinement, water, hydrogen bond, infrared spectroscopy

A groundbreaking discovery in nanofluidics was the observation of the tremendously enhanced water permeability of carbon nanotubes, those iconic objects of nanosciences. The origin of this phenomenon is still a subject of controversies. One of the proposed explanations involves dramatic modifications of the H-bond network of nano-confined water with respect to that of bulk water. Infrared spectroscopy is an ideal technique to follow modifications of this network through the inter-and intra-molecular bonds of water molecules. Here we report the first infrared study of water uptake at controlled vapor pressure in single walled carbon nanotubes with diameters ranging from 0.7 to 2.1 nm. It reveals a predominant contribution of loose H bonds even for fully hydrated states, irrespective of the nanotube size. Our results show that, while the dominating loosely bond signature is attributed to a one-dimensional chain structure for small diameter nanotubes, this feature also result from a water layer with "free" OH (dangling) bonds facing the nanotube wall for larger diameter nanotubes. These experimental findings provide a solid reference for further modeling of water behavior in hydrophobic nanochannels.

INTRODUCTION

Single walled carbon nanotubes (SWCNTs), as ultimate one-dimensional channels, represent an ideal model system for studying nanoconfinement. Water molecules, when confined inside nanochannels with diameters comparable to their size, do exhibit anomalous behavior, such as a variety of specific water ice phases [START_REF] Takaiwa | Proc. Natl. Acad. Sci[END_REF][START_REF] Mochizuki | Proc. Natl. Acad. Sci[END_REF] or tremendously enhanced water flow, as highlighted a decade ago. [START_REF] Majumder | [END_REF]4 Consequently, SWCNTs have been proposed as promising candidates for water filtration, 5,6 singlemolecule sensors, 7,8 ion selectivity, 9 or energy applications. 10 More generally, the unique behavior of water at the nanoscale is also of concern to biology, where the extreme water permeability of aquaporins, those membrane proteins developing nanopores, is crucial for many physiological processes, 11 as well as with respect to the unimpeded permeation of water through graphenebased membranes. 12,13 The water structure inside SWCNTs has been widely studied in recent years by numerical simulations. [14][15][16][START_REF] Pascal | Proc. Natl. Acad. Sci[END_REF] Although the results of these simulations strongly depend on the water model used, [16][START_REF] Pascal | Proc. Natl. Acad. Sci[END_REF][START_REF] Liu | [END_REF] the broad picture that emerges from these calculations concerns structuring phenomena, from a single-file mode in nanotubes with the smallest diameters (0.6-0.8 nm) to a layered structure for larger nanotube diameters. 15 These predictions have been confirmed experimentally by X-ray and neutron scattering, [19][20][21] nuclear magnetic resonance, 20,22,23 and resonant Raman scattering. 24 It is worth noting here that the intermolecular structure of water upon confinement departs strongly from what is observed in bulk water implying strong modifications of the H-bond network. The fast water transport through SWCNTs has been explained by several authors as due to H-bond modifications in the hydrophobic nanochannels, 14,25 while others focused on geometrical effects and curvature-induced incommensurability between water and carbon wall. 26. The origin of the ultrafast water transport in CNTs thus remains still to debate. Experimental approaches have not yet provided a guidance to theory and numerical simulations 14,25-27, . Within this framework, an experimental analysis of the H-bond network of water confined in SWCNTs thus appears mandatory. Infrared (IR) spectroscopy in a wide spectral range is a technique of choice to probe the hydrogen bond network. 28 It has been successfully applied to study water confined in hydrophilic nanometer size structures such as zeolite, 29 or silica glasses. 30 In the case of sub-2-nanometer SWCNTs, the Hbond network of confined water molecules is predicted to be markedly weaker than that in bulk water 31 . Consequently, a high frequency vibration peak should be observed in the mid-IR spectrum of water confined in SWCNTs, attributed to loosely bound molecules, i.e. those which have only established two or less bonds with neighboring molecules. But up to now, the only IR absorption measurements reported in the literature of water in SWCNTs 32,33 have failed to reveal any of the specific modifications to the vibrational spectrum predicted for water confined in carbon nanotubes. 27 In this article, we report the first infrared study, at controlled vapor pressures, of the water dynamics in single walled carbon nanotubes with diameters ranging from 0.7 to 2.1 nm. Our results reveal that water molecules confined in SWCNTs remain loosely bound even for fully filled tubes. The specific thermodynamic and transport properties of water in such nanotubes are benchmarked against these new experimental results.

EXPERIMENTAL DETAILS

Single Walled Carbon Nanotubes (>95 vol% CNT; >90 vol% SWCNT) were bought from Nanostructured & Amorphous Materials, Inc. (NanoAmor). As specified by the manufacturer, the length of the tubes is between 1 and 3 µm. Raman spectroscopy experiments performed at different incident energies (1.2, 1.9 and 2.2 eV) were used to quantify nanotubes diameters. Based on the measurements of radial breathing mode frequencies 34 they were found to range from 0.7 to 2.1 nm. Moreover, the ratio RG/D between the areas of the G and D bands is rather high (RG/D~19) indicating a small amount of defects 34 (see Figure SI1). In addition, C-O-C and C=O stretching modes bands 35 at cm -1 and 1710 cm -1 were not detected by infrared spectroscopy, confirming the structural quality of the nanotubes studied. X-ray scattering (XRS) experiments were undertaken at CuKα wavelength. The measured intensity decreases monotonously up to 1.6Å -1 (Figure SI2a). The XRS diagram of a powder of isolated nanotubes with a well-defined diameter would present wide oscillations, the minima of which being related to the diameter value (see Figure SI2b). The effect of a distribution in diameters, illustrated here for a SWCNT powder (2r = 14 Å) with diameters ranging between 6.5 and 21.5 Å (FWHM = ±7.5 Å) is also presented in Figure SI2b. In this case, the oscillations are completely vanished in agreement with the experimental XRS diagram obtained for our sample (Figure SI2a), which confirms the wide distribution of nanotubes diameters. Moreover, if nanotubes were organized in bundles, corresponding diffraction peaks should be observed, as shown in ref.

(see also Figure SI2c). The absence of such peaks strongly suggests that in our sample, SWCNTs do not form bundles, due to the wide distribution in diameters. In the frame of the present study, water filling of SWCNTs with a narrow diameter distribution around 1.4nm and forming bundles have also been measured using IR, but surprisingly these experiments did not allow obtaining exploitable water signature. A possible explanation is that part of these nanotubes being metallic and assembled in large bundles, each bundle contains some metallic SWCNTs leading to complex shielding effects. In the present sample where nanotubes do not assemble in bundles, such shielding effects are avoided for the non-metallic nanotubes.

Infrared measurements were performed at the AILES beamline of synchrotron SOLEIL. The Bruker IFS125 FT-IR spectrometer was equipped with a 6 µm Mylar beam splitter and a 1.6 K bolometer for terahertz measurements (THz range: 20-60 cm -1 at 2 cm -1 resolution), the same beam splitter and a 4.2 K bolometer for far infrared measurements (FIR range: 50-500 cm -1 at 2 cm -1 resolution), and a KBr beam splitter combined with a wide range 4.2K bolometer for mid infrared (MIR range: 500-4000 cm -1 at 4 cm -1 resolution). The sample consists of a thin layer of nanotubes deposited on a gold grid placed at normal incidence relative to the incident beam. The sample holder was placed in a hydration cell especially designed for insitu infrared measurements under controlled vapor pressure. 36 Prior to hydration, adsorbed species were removed by heating nanotubes in situ at 310 K for 12 hours under vacuum (10 -6 mbar), using a cell inlet allowing for gas input/output or turbomolecular pumping 36 . Gas dosage and measurements were performed in regulated static conditions at 298 (±0.2) K. Different values of water vapor pressure P were set, corresponding to 9%, 30%, 55% and 100% relative humidity RH=(P/P0)*100, P0 being the water vapor pressure equal to 31.7 mbar at 298 K. The equilibrium was found to be reached (no more evolution of the IR spectra) in less than one hour for all water pressures and all measurements presented here were performed after several hours at the nominal water pressure.

Transmitted intensity modifications between the dry sample and the sample under a given water pressure are relatively weak (see Figure 1A), due to the small quantity of adsorbed water and the strong scattering by nanotubes powder. Data are thus presented in absorbance difference, subtracting the absorbance of the dry sample from the one of the sample exposed to a given water pressure, in order to highlight the modifications occurring when the system passes from the dry state to a hydrated one. Absorbance difference (Figure 1B) is defined as A=-log(I/Io) where I and I0 are respectively the transmitted intensities with a given value of relative humidity (RH=100% in Figure 1A ) and in the dry state (black curve in Figure 1A). Positive absorbance is therefore related to water molecules sorption in the sample and to residual water vapor present in the cell during hydration. In order to remove the water vapor contributions, appearing as narrow structures on the spectra (red part in Figure 1B), additional experiments were performed in the same humidity conditions, with the sample holder (and the gold grid) but in the absence of CNTs, as shown in Figure 1C. The expanded curve in figure 1C evidences a broad structure centered at 3370 cm -1 characteristics of condensed water. This feature shows that a tiny quantity of water (less than 5% of the total amount of water adsorbed in CNTs at RH=100%) is deposited on surfaces present in the light path (diamond cell windows or sample holding gold grid). The precise measurement of these water molecules contribution (vapor water as well as liquid water deposited on the sample holder or on the diamond windows) allows the spectra of hydrated CNT to be corrected and the specific spectral signature of water sorption in CNTs to be determined (Figure 1D). Difference spectra of the hydration sequence were performed in the THz (20-50 cm -1 ), FIR (50-500 cm -1 ) and MIR (500-4000 cm -1 ) regions; the observed absorption bands are displayed in Figure 2 from the dry state (bottom) to RH=100%. One can observe the OH stretching band between 3100 and 3700 cm -1 (Figure 2D), the water bending mode around 1625 cm -1 (Figure 2C), its libration mode around 560 cm -1 (Figure 2B), as well as a low frequency mode at 40 cm -1 (Figure 2A). These experimental results can be compared with the calculated IR spectra in ref. 27, where Martí and Gordillo considered water confined inside SWCNTs with diameters ranging from 0.8 to 1.6 nm (see Table 1). The calculated spectra exhibit clear stretching, bending and libration modes, in agreement with our experimental spectra (Figure 2). In our experiments, we notice that the bending mode of water, at 1625 cm -1 , is red shifted by ~ 35 cm -1 compared to bulk water bending, typically observed at 1660 cm -1 . 37 Martí and Gordillo have also predicted a red shifted bending vibration at 1615 ± 10 cm -1 for water confined in SWCNTs. 27 In a similar manner, the libration at 560 cm -1 appears red shifted compared to the bulk value (680 cm -1 ) in good agreement with predictions for water inside nanotubes. 27 The assignment of the mode at 40 cm -1 is less straightforward, although its intensity gradually increases upon water sorption (see Figure 2), like those of the libration, bending and stretching modes, confirming that it should be attributed to water progressively encapsulated inside SWCNTs. It is worth noting that such a low energy feature had also been predicted by Martí and Gordillo 27 who based their assignment on a comparison with a Raman study on bulk water by Walrafen 38 . This author attributes the low energy band to the bending of hydrogen bonded O-O-O unit. Alternative assignment for bulk water may imply translations frustrated by cage effects independently of the hydrogen bond 39 . One can also discuss this mode in connection with numerical simulations for water in CNT by Kumar and coworkers 40 . Within this framework, it should be attributed to a transverse translational motion of a one-dimensional chain of water in the smallest diameter nanotubes (D ~ 0.8nm), which would thus be observed here for the first time. Such translation could be made infrared active by the preferential orientation of molecules trapped within the tube allowing for an induced dipolar moment.

RESULTS AND DISCUSSION

Let's now consider the stretching band, the shape of which is found to be noticeably different from that of bulk water (Figure 3). The OH stretching band of water provides detailed information about its networking. Indeed, the oscillator strengths and thus associated vibrational frequencies decrease when coordination number increases. 30 Stretching bands of bulk liquid and ice are reported in Figure 3. Both are well fitted with three Gaussian functions. For liquid water (Figure 3B down), the high energy Gaussian is assigned to loosely bound water molecules (two, one or zero H-bonds), the lowest energy is representative of highly connected water (four H-bonds) and the intermediate energy component is representative of water with a coordination number close to three. 37,41 For ice water Ih (Figure 3A down), a shift towards low frequencies is observed as the result of the establishment of the rigid tetrahedral network. In the 3100-3500 cm -1 range, the absorbance of water confined in SWCNTs presents similarities either with bulk ice Ih, at RH=30%, or with bulk water, at RH=100%. Pascal and co-workers [START_REF] Pascal | Proc. Natl. Acad. Sci[END_REF] have discussed different stabilization mechanisms for water inside SWCNTs, depending on the nanotube diameter. In particular, an enthalpy stabilized, ice-like phase is predicted at room temperature for nanotube diameters of 1.1-1.2 nm while a liquid-like phase, stabilized by the increase of translational entropy, is predicted for nanotubes diameters above 1.4 nm. It is also known that water adsorption isotherms in SWCNTs are of type V, characterized by a low water uptake followed by a complete filling of the tubes until a threshold pressure is reached. 42 In addition, this threshold shifts to larger RH values when increasing the nanotube diameter. 32,42 In consequence as smaller diameter nanotubes start trapping water first, the ice-like component visible at RH=30% in Figure 3A can be attributed to a peculiar ice-like nanoconfined water formed in intermediate size nanotubes as corroborated by the recent X-ray diffraction results of Kyakuno et al. 20 At higher water pressure, the filling of larger nanotubes dominates. 32,43 Accordingly, the proportion of liquid-like confined water increases which is demonstrated by the appearance of a liquid-like contribution in the IR spectrum in Figure 3B.

A major difference between water confined in SWCNTs and bulk water is the presence of intense high frequency components between 3500-3700 cm -1 . This important feature is a clear signature of loosely bound water molecules, due to the effect of confinement. 27 This observation is truly original with respect to all experimental results on the dynamics of water inside SWCNTs published so far. 19,32,33,44 Indeed, Kolesnikov et al. 19 focused on large amplitude motions of H in SWCNTs while Byl et al. 44 were concerned with nanoconfined ice at low temperature. The IR spectra in ref. 32 are unfortunately dominated by vapor pressure signatures while, surprisingly, room temperature IR study in ref. 33 does not show any specific confined water signature. It should also be underlined that, according to numerical simulations, a main difference between bulk water and water confined in SWCNTs is its reduced number of H-bonds 14,15,25,31,[45][46][47] , leading to high frequency components for the confined water stretching band, 27,48 in agreement with our measurements. In order to provide a complete picture, it is worth noting that water outside of carbon nanotubes can form a layered structure, as evidenced recently in photoluminescence experiments. 49 Gordillo and Martí also considered water outside CNT in ref 50 and 51. Based on their simulations, the O-H high energy features dominating our spectra for every hydration level cannot be attributed to water around the nanotubes (indeed, only water confined between three SWCNTs within a bundle would present a high energy vibration, while in our sample, SWCNTs are not organized in bundles). Similarly, no major high frequency contribution is predicted for water at the surface of a graphene sheet 45 . We thus conclude that water outside the nanotubes cannot explain the observed peculiar shape of the stretching band.

A semi-quantitative analysis of the evolution of the stretching band with vapor water pressure is proposed, based on its deconvolution with five Gaussian functions G1, G2, G3, G4 and G5 centered at 3215, 3340, 3435, 3560 and 3640 cm -1 , respectively (Figure 4). This number of independent component Gaussians was verified, by singular value decomposition 52 . Using this method, the total absorbance data at different relative humidity can be described with better than 99% accuracy by the first 5 basis functions (notice that the weakest of these 5 singular values contributes to 1.5 % as compared to less than 0.01 % for a sixth contribution, a value close to the spectra noise floor). This implies that a minimum of 5 independent Gaussians are required to describe the data. The fitting of the stretching region was realized using the Levenberg-Marquardt algorithm to search for the minimum value of  2 . The relative areas of each subcomponent are used to quantify the evolution of the H-bond network during the filling of SWCNTs. In the first step of filling (RH=9%, Figure 4A), the stretching band is mainly composed of (85%) the high frequency components G4 and G5. This feature can be attributed to loosely bound water with coordination number less or equal to two, forming one-dimensional (1D) chains in the narrowest nanotubes with diameter around 0.8 nm, 14,27 or forming very local clusters. 47 The presence of such high frequency OH sub component may indeed reflect two situations. If they are the only OH components, they strongly support the assembling in 1D chains as these are the structure formed from one and two bound water molecules only. When they are present together with other sub-comments at lower frequency, they can also result from clusters with dangling OH bonds pointing toward the carbon walls. At Rh=9%, a low energy contribution G1 is observed, which corresponds to ice-like n-gonal configurations and suggests the filling of nanotubes with diameters of 1.1-1.2 nm. [START_REF] Pascal | Proc. Natl. Acad. Sci[END_REF]47 while the high frequency component (G4+G5) is attributed to 1D chains in the smallest diameter nanotubes. This analysis is in agreement with the filling of water inside the smallest tubes at first, in accordance with adsorption isotherms for various tube diameters. 32,42 As the water relative humidity increases (RH=30%), the amount of strongly bound water molecules increases, as demonstrated by the increase of the two low frequency components G1 and G2, Figure 3B. For RH≥50%, in Figure 4C,D, an intermediate component, G3, appears with a significant intensity at 3435 cm -1 . The spectral distribution of OH stretching band between 3100 and 3500 cm -1 then resembles its bulk liquid counterpart (see Figure 3B) supporting an H-bond network similar to bulk water. Accordingly, G3 appearance is concomitant with that of the libration band (Figure 2B). A progressive structuration of water in layers was previously observed by X-ray scattering during 1.4 nm diameter nanotubes filling 21 and such structuration is also expected to occur in nanotubes with diameters above 1.4 nm. [46][47] A non-negligible part of the high frequency signature (G4 and G5) for RH≥50% should thus be attributed to the molecules in the layer closest to the carbon wall inside SWCNTs, with "free" OH bonds (dangling bonds) pointing towards the nanotube wall. Notice that simulations for water outside the carbon nanotube 50,51 also predict a broad feature centered around 3350 cm -1 . Consequently, on the basis of this comparison, one cannot exclude a contribution from water outside the tubes at hydration level above 50 %, however for hydration below 50%, such contribution is negligible or absent.

We verified the trapping of water molecules using this time D2O vapor pressure. The same sample previously hydrated with H2O was pumped for several hours before hydration with D2O. We present here the measurement on O-D stretching band where the largest isotopic effect is present. The inset allows a comparison with the bulk D2O absorbance. Compared to the bulk, the O-D stretching band for D2O in SWCNTs presents an intense highfrequency component (Figure 5) as it is also the case for confined H2O. The SWCNTs absorbance difference extends from 2300-2750 cm -1 a range shifted by the ratio of the square root of reduced mass compared to the O-H stretching 37 . Furthermore, the trapped water O-D is composed of five sub-components each precisely shifted (within 1%) compared to the OH subcomponents and therefore confirming their similar assignment (cf Table 2). One may notice however, that in the D2O case, the high frequency subcomponents are not quite as dominant. It is worth noting that isotopic differences in band shape are also observed in the bulk stretching bands. Moreover, as shown in Figure 1A, this spectral domain corresponds to strong variations of the intensity transmitted by the diamond windows of the humidity cell, which limits our precision on the absolute intensity values. In a context where our knowledge of the detailed water structure inside or outside SWCNTs is mainly based on numerical approaches, the present experimental data strongly suggest that (i) the 1D structure of confined water in small diameter (0.8 nm) nanotubes, (ii) its ice-like structure in medium diameter (1.1-1.2 nm) nanotubes and (iii) the presence of a layer with "free" OH bonds facing the nanotube inner (or outer) wall for liquid water in larger diameter (≥1.4 nm) nanotubes. While our experimental data cannot be used to ascertain the presence of water layers outside nanotubes, their signal, if any, may reinforce that of the water layer facing the nanotube inner wall. Furthermore, our experimental results show that the water network is strongly affected by confinement and that more than half of the intensity of the stretching band comes from loosely bound water molecules, even for fully hydrated nanotubes.

The observation of ice-like water corroborates the theoretical approach developed to explain the spontaneous filling of hydrophobic carbon nanotubes, 17 a counterintuitive phenomenon. Our results also support theoretical studies about the ultra-low friction of water through SWCNTs. Indeed, besides geometrical effects, 26 it was claimed that the 1D structure of water inside the smaller diameter nanotubes or the peculiar hydrogen bonding at the interface in larger ones significantly eases the flow rates. 14,25 The effect of one-dimensional confinement versus two-dimensional confinement could be elucidated by studying graphene-like membranes which were shown to be highly permeable to water, a property attributed to low friction flow of a monolayer of water between closely spaced graphene sheets. 13 In this case, strong modifications of the H bonds networks and of the corresponding IR spectrum are also predicted 28 and the experimental approach developed here should provide crucial information about water properties in graphenebased membranes. Finally, one should underline that simulations of nanoconfined water properties are mainly based on phenomenological models, each designed to explain specific bulk water properties, 15 with modeldependent results. 16,[START_REF] Liu | [END_REF] The present experimental data provide a solid reference for the elaboration of an energetic model accounting for the properties of water in hydrophobic nanochannels.

Figure 1 .

 1 Figure 1. Data processing steps, on the example of SWCNTs under H2O vapor pressure, for RH = 100%. (A) Measurement of transmitted intensity through dry CNT (I0) and hydrated CNT (B) Absorbance -log(I/Io) of water inside SWCNTs calculated from A. (C) Absorbance of water vapor inside the hydration cell under the 100% relative humidity in the empty cell under the same experimental conditions than (B), used as reference. The expanded curve presented above shows a minor contribution from water deposited on cells elements

Figure 2 .

 2 Figure 2. Absorbance of water inside SWCNTs from dry state to 9%, 30%, 55%, and 100% of relative humidity. Water content increases from bottom to top. Different spectral ranges are shown to highlight vibrational modes of adsorbed water during hydration: (A) around 40 cm -1 ; (B) around 560 cm -1 (C) around 1625 cm -1 ; (D) between 3100 and 3700 cm -1 . Spectra are translated vertically for the sake of clarity.

Figure 3 .

 3 Figure 3. (A) OH stretching bands (3100-3740 cm -1 ) of water adsorbed in SWCNTs at RH=30% (top) and bulk ice water (bottoms. The ice-like contribution of confined water at low frequencies is highlighted in blue. (B) O-H stretching bands of water adsorbed in SWCNTs at RH=100% (top) and

Figure 4 .

 4 Figure 4. Deconvolution of the O-H stretching band with five Gaussians for relative humidity of 9% (A), 30% (B), 55% (C), and 100% (D). Inset: proportion (in percent) of each subcomponent.

Figure 5 .

 5 Figure 5. Absorbance of D2O inside SWCNTs from dry state to 5%, 20%, 60%, 77% and 100% of relative humidity. Water content increases from bottom to top. Inset comparison bulk D2O and D2O inside SWCNTs at RH=100%.

Table 1 .

 1 Main component of confined water bands for H2O and comparison with simulation for various tube diameter from ref 27.

			Mode frequency (cm -1 )
	Experimental results	Simulation results (from ref. 27)
	Assignment		Assignment		tube diameter
				8.2 Å	10.9 Å	13.6 Å	16.3 Å
	Translation	40	Translation	~30	~50	~50	~50
	Libration	560	Libration	400	420	448	472
	Bending	1625	Bending	~1625	~1615	~1615	~1605
	Stretching G1	3215
	Stretching G2	3340	Symmetrical stretching	3363	3420	3412	3411
	Stretching G3	3435	Asymmetrical stretching	3707	3662	3657	3641
	Stretching G4	3569
	Stretching G5	3640

in CNT Experimental (cm -1 ) O-D in CNT Calculated (cm -1 ) O-D in CNT Experimental (cm -1 )

				Difference
				calculated /
				experimental
				(%)
	3215	2347	2364	0.7
	3340	2438	2479	1.7
	3435	2507	2535	1.1
	3569	2605	2606	0.02
	3640	2657	2687	1,1

O-H

Table 2 .

 2 Main

	sub-component of confined water
	stretching bands for H2O and D2O, and comparison of their
	frequencies. 1 st and 3 rd columns: measured values for H2O
	and D2O. 2 nd column calculated value of D2O frequencies
	based on isotopic shift applied to H2O experimental values.
	4 th column: relative difference between measured and
	calculated D2O frequencies.

ACKNOWLEDGMENT

We benefited from helpful discussions with Laurent Manceron, Stéphane Rols and Fransceco Capitani. We also acknowledge Rozenn Le Parc and Jean-Louis Bantignies for Raman analysis of the nanotubes diameters. Synchrotron SOLEIL is acknowledged for providing beamtime and for financial support during Simona Dalla Bernardina's Ph.D training. We thank John Bozec for a critical reading of the manuscript. EP, SR and PL benefited of a grant from the French Research National Agency under the program Investissements d'Avenir (ANR-10-LABX-0035: Labex NanoSaclay).

AUTHOR INFORMATION

Corresponding Author *E-mail: (P.R.) pascale.roy@synchrotron-soleil.fr (P.L.) pascale.launois@u-psud.fr;

Supporting Information Available

Raman spectrum of the dried nanotubes measured at wavelength = 514 nm ( 2.41 eV). Details of X-Ray scattering (XRS) experiments on the dry SWCNT powder, XRS simulation formalism and additional figures This material is available free of charge via the Internet at http://pubs.acs.org.