Francesca Branzoli
email: francesca.branzoli@icm-institute.org

Dinesh K Deelchand

Roberto Liserre

Pietro Luigi Poliani

Lucia Nichelli

Marc Sanson

Stéphane Lehéricy

Malgorzata Marjańska

Małgorzata Marjańska

The influence of cystathionine on neurochemical quantification in brain tumor in vivo magnetic resonance spectroscopy

Keywords: 1 H MRS, glioma, cystathionine, metabolite quantification

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

Introduction

In vivo [START_REF] Keunen | Is there a prominent role for MR spectroscopy in the clinical management of brain tumors?[END_REF] H magnetic resonance spectroscopy (MRS) enables the noninvasive detection and quantification of a series of neurochemicals in brain tissue. Thanks to the wider access to high magnetic field MRI systems and the significant advances in MR methodologies, in the last years, MRS has shown great promise for the noninvasive investigation of abnormal metabolic changes in brain tumors and for the identification of tumor genetic subtypes in vivo, providing added diagnostic and prognostic value to other MRI modalities [START_REF] Keunen | Is there a prominent role for MR spectroscopy in the clinical management of brain tumors?[END_REF][START_REF] Branzoli | Magnetic resonance spectroscopy of isocitrate dehydrogenase mutated gliomas: current knowledge on the neurochemical profile[END_REF][START_REF] Ly | Imaging of Central Nervous System Tumors Based on the 2016 World Health Organization Classification[END_REF] . In particular, since the discovery in 2009 of isocitrate dehydrogenase (IDH) mutations in gliomas [START_REF] Dang | Cancer-associated IDH1 mutations produce 2hydroxyglutarate[END_REF][START_REF] Parsons | An integrated genomic analysis of human glioblastoma multiforme[END_REF] , several molecules other than N-acetyl aspartate (NAA), choline containing compounds (tCho) and lactate have been shown to be detectable in vivo by MRS and identified to play crucial roles in the reprogramming of cancer cell metabolism. These include, 2-hydroxyglutarate (2HG), a specific marker of IDH-mutated gliomas [START_REF] Andronesi | Detection of 2-hydroxyglutarate in IDHmutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy[END_REF][START_REF] Choi | 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas[END_REF] , citrate, an intermediate for energy generation and biosynthesis of lipids and related molecules [START_REF] Icard | Understanding the central role of citrate in the metabolism of cancer cells[END_REF][START_REF] Choi | In vivo detection of citrate in brain tumors by 1 H magnetic resonance spectroscopy at 3T: MRS Detection of Citrate in Brain Tumors[END_REF] , and glycine, a putative marker or tumor aggressiveness irrespective of the IDH status [START_REF] Tiwari | Glycine by MR spectroscopy is an imaging biomarker of glioma aggressiveness[END_REF] .

Many other metabolites present in cancer cells at low concentrations and suggested to be related to malignancy can be reliably detected in tumor tissue by mass spectrometry ex vivo (e.g., aspartate, glutathione, phosphorylethanolamine, taurine, hypotaurine, threonine, ornithine, betaine, serine) [START_REF] Elkhaled | Magnetic Resonance of 2-Hydroxyglutarate in IDH1-Mutated Low-Grade Gliomas[END_REF] . The concentration of some of these neurochemicals may reach the threshold of detectability by MRS for high tumor cell densities or in specific brain tumor subtypes, either appearing as "unknown" signals in the spectra or being fitted by the fitting software as another compounds whose concentration is therefore overestimated. So far, due to the heterogeneity of brain tumors and their metabolism, no consensus on which metabolites should be included in the analysis of tumor spectra has been reached. Because of the large overlap between certain metabolic peaks, in conjunction with the low concentration of these molecules, the omission of a spectral component in the analysis may have a strong influence on the detection and quantification of other overlapping signals, especially when using conventional MRS. Spectral overlap can be partially removed using edited MRS methods [START_REF] De Graaf | Vivo NMR Spectroscopy -Principles and Techniques[END_REF] , yet the analysis of non-edited spectra is useful for the quantification of the full metabolic profile and conventional MRS remains the most commonly used technique especially in clinical settings.

Recently, we reported the first detection of cystathionine in gliomas by edited MRS [START_REF] Branzoli | In vivo 1 H MRS detection of cystathionine in human brain tumors[END_REF] .

Cystathionine was not detectable in healthy tissue and was therefore proposed as novel noninvasive marker of tumor tissue. In addition, cystathionine was found to be present at significantly higher concentrations in IDH-mutated gliomas with 1p/19q codeletion compared to their non-codeleted counterparts [START_REF] Branzoli | Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy[END_REF] .

In this study, we retrospectively analyzed the spectra acquired previously using conventional MRS optimized for 2HG detection in a cohort of patients with glioma and with detectable cystathionine levels measured from edited MRS. We evaluated the ability of optimized PRESS for cystathionine detection and reported the effect of the omission of cystathionine on the quantification of the full neurochemical profile, thus highlighting the importance of including this metabolite in the spectral fitting of in vivo tumor MRS data.

Methods

Human subjects

Twenty-seven subjects (18 males, median age: 41 years, range: 24 -53 years) with a glioma were included in the study. Of these, 17 subjects were recruited at the Pitié-Salpêtrière Hospital, Paris, France, and examined at the Centre for Neuroimaging Research (Site 1), while the remaining 10 subjects were recruited at the Spedali Civili University Hospital, Brescia, Italy (Site 2).

Twenty-three subjects (1 IDH wild-type and 22 IDH-mutated, of which 14 1p/19q codeleted, 7 1p/19q intact, 1 with unknown codeletion status) were retrospectively selected based on the presence of the cystathionine signal in the in vivo edited MR spectrum, assessed by two expert MR spectroscopists, and the availability of both MEGA-PRESS and optimized PRESS acquisitions in tumor. For four of these subjects, the PRESS acquisition was performed also in the contralateral region outside the visible lesion. For additional four subjects, only the MRS of the healthy side of the brain was considered for this study.

Additional inclusion criteria were: age > 18 years and Karnofsky performance status > 60.

Patients recruited at site 1 provided written informed consent prior to inclusion in the study, while patients recruited at site 2 were examined as part of their clinical care.

In vivo MRI/MRS acquisition

Acquisitions were performed using 3 T whole-body systems (MAGNETOM Verio -Site 1, and Skyra -Site 2, Siemens, Erlangen, Germany), using a 32-and a 20-channel receive-only head coils, respectively. 3D FLAIR images (site 1: field-of-view = 255 x 255 x 144 mm 3 , resolution: 1.0 x 1.0 x 1.1 mm 3 , T R /T E = 5000/399 ms, scan time = 5.02 minutes; site 2: field-of-view = 242 x 227 x 176 mm 3 , resolution: 0.5 x 0.5 x 1.0 mm 3 , T R /T E = 5000/394 ms, scan time = 6.27 minutes) were acquired to position the spectroscopic voxel of interest (VOI) in the glioma (hyper-intense region in the images). A 3D T 1 -weighted sequence (field-of-view = 256 x 256 x 176 mm 3 , resolution: 1.0 x 1.0 x 1.0 mm 3 , T R /T E = 2300/4.18 ms, scan time = 4.28 minutes) was also acquired at site 1 and used for tissue segmentation of healthy brain tissue.

MR spectra were acquired at both sites using the same single-voxel spectral editing MEGA-PRESS (T R = 2 s, T E = 68 ms, 128 pairs of shots, scan time = 8.5 minutes) and PRESS sequences (T R = 2.5 s, T E = 97 ms, T E1 = 32 ms, T E2 = 65 ms, 128 shots, scan time = 5.45 minutes), and employing previously described procedures and parameters [START_REF] Mescher | Simultaneous in vivo spectral editing and water suppression[END_REF] . For both sequences, PRESS spatial localization utilized a 90º Hamming-filtered sinc pulse (duration = 2.32 ms; bandwidth = 3.83 kHz) and two 180º Mao pulses (duration = 5.80 ms; bandwidth = 1 kHz). For MEGA-PRESS, the editing pulse (180º Shinnar-Le Roux; duration = 19.2 ms; bandwidth = 62 Hz) was applied at 1.9 ppm for the edit-on condition and at 7.5 ppm for the edit-off condition, in an interleaved fashion. The final spectra were obtained by subtracting the spectra acquired at the edit-on and edit-off conditions. For each subject, the VOI size was adapted to the tumor size. The mean VOI size was 11.4  7.5 mL (range: 7.2 -33.4 mL) and 13.8  2.6 mL (range: 7.5 -15.6 mL) for Site 1 and Site 2, respectively.

Water suppression was performed using variable power with optimized relaxation delays (VAPOR) and outer volume suppression techniques [START_REF] Tkac | In vivo 1 H NMR spectroscopy of rat brain at 1 ms echo time[END_REF] . Unsuppressed water scans were acquired from the same VOI for metabolite quantification and eddy current corrections using the same parameters as water suppressed spectra. B 0 shimming was performed using a fast automatic shimming technique with echo-planar signal trains utilizing mapping along projections, FAST(EST)MAP [START_REF] Gruetter | Field mapping without reference scan using asymmetric echoplanar techniques[END_REF] .

Spectral processing and quantification

Single-shots were frequency and phase aligned in Matlab (MathWorks Inc., Natick, MA) using the tCho signal at 3.22 ppm. All spectra were analyzed using LCModel v6.3-0G [START_REF] Provencher | Estimation of metabolite concentrations from localized in vivo proton NMR spectra[END_REF] (Stephen Provencher, Inc., Oakville, ON, Canada) with the basis sets simulated using the density matrix formalism [START_REF] Henry | Proton-observed carbon-edited NMR spectroscopy in strongly coupled second-order spin systems[END_REF] as previously described [START_REF] Branzoli | Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy[END_REF] . RF duration and patterns for 90° and 180° pulses, slice-selective gradients during 180° pulses, timing and previously published chemical shifts and J-couplings [START_REF] Branzoli | In vivo 1 H MRS detection of cystathionine in human brain tumors[END_REF][START_REF] Govindaraju | Proton NMR chemical shifts and coupling constants for brain metabolites[END_REF][START_REF] Kaiser | 1H MRS detection of glycine residue of reduced glutathione in vivo[END_REF][START_REF] Wishart | HMDB: a knowledgebase for the human metabolome[END_REF][START_REF] Stefan | Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package[END_REF] were taken into account. The basis set used for MEGA-PRESS included 2HG, cystathionine, γ-aminobutyric acid (GABA), glutamate, glutamine, glutathione, NAA, N-acetylaspartylglutamate (NAAG). The basis set for PRESS included 2HG, alanine, ascorbate, aspartate, betaine, citrate, cystathionine, creatine, cysteine, ethanolamine, GABA, glucose, glutamate, glutamine, glutathione, glycerophosphorylcholine, glycine, myo-inositol, lactate, NAA, NAAG, phosphorylcholine, phosphorylethanolamine, scyllo-inositol, serine, succinate, taurine and threonine. Spectra were fitted between 1.8 and 4.2 ppm for the MEGA-PRESS acquisition, and between 0.5 and 4.1 ppm for the PRESS. The DKNTMN parameter was set to 5, which results in a stiff baseline, suitable for data free from artifacts. For the PRESS data, the LCModel analysis was repeated twice, with and without cystathionine in the basis set. For tumor data, the quantification was carried out by scaling the signal using the unsuppressed water reference, assuming a tumor bulk water concentration of 43.3 M, as done previously [START_REF] Choi | 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas[END_REF] . Water and metabolite relaxation effects were compensated using water transverse and longitudinal relaxation time constants (T 2 and T 1 ) of 150 ms [START_REF] Madan | Proton T 2 measurement and quantification of lactate in brain tumors by MRS at 3 Tesla in vivo: lactate T 2 measurement in brain tumors[END_REF][START_REF] Berrington | Improved localisation for 2-hydroxyglutarate detection at 3T using long-TE semi-LASER[END_REF] and 800 ms [START_REF] Rooney | Magnetic field and tissue dependencies of human brain longitudinal1H2O relaxation in vivo[END_REF] , respectively, and previously reported metabolite T 2 and T 1 values [START_REF] Deelchand | Transverse relaxation time constants of the five major metabolites in human brain measured in vivo using LASER and PRESS at 3 T[END_REF][START_REF] Träber | 1 H metabolite relaxation times at 3.0 tesla: Measurements of T1 and T2 values in normal brain and determination of regional differences in transverse relaxation: 1 H Metabolite Relaxation Times at 3.0 T[END_REF] . For J-coupled metabolites with unknown relaxation time constants, glutamate T 2 and T 1 were used. For healthy tissue data, cystathionine concentrations were scaled assuming a water concentration of 41.8 M (based on tissue content in the VOI, averaged across subjects), water T 2 and T 1 of 80 ms and 800 ms, respectively, and the same cystathionine T 2 and T 1 as in tumor data. The reported concentrations are semi-quantitative.

Metabolites that were quantified from PRESS data with Cramér-Rao lower bounds (CRLB) < 50% for more than half of the subjects were included in the statistical analysis. For PRESS data, the linewidths of total creatine (tCr) at 3.03 ppm were determined from the LCModel fit as the full width at half maximum of this peak.

In vitro MRS acquisition

A cystathionine phantom ([cystathionine] = 1 mM, pH 7.2) was prepared using a phosphate buffer with 4,4,-dimethyl-4-silapentane-1-sulfonic acid added for chemical shift referencing and measured at physiological temperature. An MRS acquisition was performed using a 3 T whole-body Siemens Prisma fit system (Siemens Medical Solutions, Erlangen, Germany) equipped with a 32-channel receive-only Siemens head coil. MR spectra were measured using a single-voxel PRESS sequence (T R = 3 s, T E = 97 ms, 256 shots, VOI = 8 cm 3 ) as used in the in vivo acquisitions.

Statistical analysis

A two-tailed paired t-test was used to compare metabolite concentrations derived from optimized PRESS data using the full basis set to those estimated without cystathionine in the LCModel analysis. In order to account for multiple comparisons, a p-value of 0.05/(number of tested metabolites) was considered statistically significant. A two-tailed paired t-test was also used to compare cystathionine concentrations obtained from optimized MEGA-PRESS and PRESS data, while simple linear regression analysis was used to assess correlations between the cystathionine concentrations obtained using these two methods.

Results

All the 23 subjects selected for this study based on visual inspection of difference spectra acquired using the MEGA-PRESS sequence had detectable cystathionine levels > 1 mM in glioma tissue. An example of in vivo difference spectrum is shown in Figure 1A, together with the cystathionine component fitted with LCModel. The cystathionine signal at 2.72 ppm can be easily identified due to the absence of overlap with other metabolites and very characteristic spectral pattern. The mean cystathionine concentration ( SD) was 3.49  1.17 mM (range: 1.73 -5.53 mM) and the associated mean CRLB ( SD) was 13  6 % (range: 7 -33%).

The mean cystathionine concentration estimated using the optimized PRESS sequence was 2.20  0.80 mM, with mean CRLB = 14  6 % (range: 6 -30%). High quality spectra were acquired at both sites. Individual spectra are shown in Supporting Information Figures S1 andS2. The mean tCr linewidth was 5.0  1.2 Hz (range 3.8 -7.2) and 5.1  1.2 Hz (range 3.6 -7.3 Hz) for Site 1 and Site 2, respectively. Despite the significant difference in the mean values (p =2 x 10 -6 ), cystathionine concentrations obtained from optimized PRESS spectra showed a significant correlation with those obtained from MEGA-PRESS data (Figure 2, r = 0.58, p = 0.004). Figure 1B shows an example of an in vivo spectrum acquired in glioma using the optimized PRESS, together with the in vitro cystathionine spectrum, LCModel fits derived with and without cystathionine in the basis set, separate fits of cystathionine, aspartate, betaine, citrate, GABA and serine, baselines and residuals. The simulated cystathionine signal well matched the phantom spectrum, confirming the accuracy of cystathionine chemical shifts and Jcoupling constants used for the simulation [START_REF] Branzoli | In vivo 1 H MRS detection of cystathionine in human brain tumors[END_REF] .

The cystathionine signal is visible at 2.72 ppm also in the PRESS spectrum, however, due to the presence of other resonances around this frequency (aspartate, citrate), the exclusion of cystathionine from the LCModel analysis had only a slight effect on the quality of the fit and the residuals. Nevertheless, the omission of cystathionine from the basis set had a significant impact on the quantification of other metabolites. Table 1 reports the metabolite concentrations and CRLBs obtained from optimized PRESS data with and without cystathionine in the LCModel analysis. Nineteen metabolites, including cystathionine, were quantified with CRLB < 50% for more than half of the subjects. The threshold for statistical significance was then set to p = 0.0028 (0.05/19 metabolites). The metabolites that were most affected by the cystathionine omission were aspartate, betaine, citrate, GABA and serine, showing a percent difference in concentration between the two methods higher than 10% and p < 0.0028 (Figure 3). Aspartate showed a significant correlation with cystathionine (r = 0.43, p = 0.04) and the mean correlation coefficient reported by LCModel between these two metabolites was -0.53  0.09 (range: -0.39 --0.70).

In normal brain tissue, the mean cystathionine concentration was 0.27  0.30 mM (range: 0-0.90 mM), with mean CRLB = 450  456% (range: 53 -999%). The mean tCr linewidth in the normal brain tissue was 5.2  0.7 Hz (range: 4.0 -5.9 Hz). An example of an in vivo spectrum acquired in normal brain tissue using the optimized PRESS is shown in Figure 1C.

Individual spectra are shown in Supporting Information Figure S3.

Discussion

In this study, we reported on the detectability of cystathionine by a PRESS sequence optimized for 2HG detection and investigated the effect of omitting cystathionine in the spectral analysis on the quantification of the full metabolic profile. The omission of cystathionine caused significant differences in the quantification of several metabolites, notably aspartate, betaine, citrate, GABA and serine.

We previously showed that cystathionine was detectable by MEGA-PRESS at higher concentrations in 1p/19q codeleted gliomas compared to non-codeleted gliomas, in agreement with ex vivo mass spectrometry experiments, while it was not detectable in normal brain tissue [START_REF] Branzoli | Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy[END_REF] . The omission of cystathionine in the analysis of non-edited spectra may therefore lead to the wrong evaluation of other metabolic changes in tumors.

Cystathionine has a complex spectroscopic profile with several multiplets, which, at 3 T collapse into four main multiplets at around 2.2, 2.7, 3.1 and 3.8 ppm [START_REF] Branzoli | In vivo 1 H MRS detection of cystathionine in human brain tumors[END_REF] . Under physiological conditions, all cystathionine resonances overlap with signals of other compounds in nonedited spectra.

For this study, patients with glioma were retrospectively selected based on the presence of cystathionine in in vivo edited spectra. Edited MRS removes the overlap between the cystathionine signal at 2.72 ppm and other signals resonating around the same frequency [START_REF] Branzoli | In vivo 1 H MRS detection of cystathionine in human brain tumors[END_REF] .

The presence of cystathionine was assessed visually from difference spectra (Figure 1A), and then confirmed and quantified by LCModel analysis.

The metabolites included in the basis set for analysis of optimized PRESS data were chosen based on previous reports on tumor tissue metabolic data measured in vivo by MRS and ex vivo by mass spectrometry [START_REF] Branzoli | Magnetic resonance spectroscopy of isocitrate dehydrogenase mutated gliomas: current knowledge on the neurochemical profile[END_REF] . Cystathionine was detectable by PRESS in glioma with CRLB ≤ 30%, while it was not detectable in the healthy brain tissue (CRLB > 50%). Cystathionine concentrations significantly correlated with those quantified using MEGA-PRESS. However, the mean cystathionine concentration was significantly lower from optimized PRESS than that obtained using edited MRS. This is most likely due to the ability of LCModel to correctly assign spectral patterns to different neurochemicals in different spectra. In MEGA-PRESS, there is no overlap of cystathionine with other metabolites. In optimized PRESS, there is an overlap of cystathionine with other neurochemicals, which results in the mean correlation coefficient reported by LCModel between cystathionine and aspartate of -0.53 and significant correlation between the concentrations of these two metabolites. Although, some of the discrepancy might be also coming from differences in T E and T R used between these methods since assumed T 2 and T 1 values were used for calculating final concentration values.

The metabolites that were most affected by the omission of cystathionine from the spectral analysis were the J-coupled metabolites with overlapping resonances with cystathionine signals. Aspartate was the most affected, showing a 2.69-fold higher concentration when cystathionine was not part of the basis set (Table 1), due to its multiplets, in proximity of 2.7 ppm and 3.8 ppm, both overlapping with cystathionine resonances. Similarly, citrate and GABA showed 1.5 and 1.39-fold increases, respectively, when omitting cystathionine from the analysis, due to the overlap of their coupled resonances around 2.6-2.7 ppm with cystathionine. Betaine was 1.11 times higher when cystathionine was not part of the analysis, while serine concentration was significantly reduced, likely due to the singlets of these molecules at 3.8 ppm overlapping with cystathionine.

The precise role of these metabolites in cancer metabolisms is still not fully known. Increased aspartate and betaine were found, among other metabolites, to be significantly elevated ex vivo in lesions that had undergone malignant progression to grade IV compared with all other grades [START_REF] Jalbert | Metabolic Profiling of IDH Mutation and Malignant Progression in Infiltrating Glioma[END_REF] . Ex vivo experiments also showed significantly lower GABA levels in IDH-mutated vs. IDH-wild-type gliomas and a positive correlation with 2HG levels in recurrent tumors [START_REF] Jalbert | Metabolic Profiling of IDH Mutation and Malignant Progression in Infiltrating Glioma[END_REF] .

GABA concentration is tightly linked with available pools of glutamate and glutamine, which were also shown to be reduced in IDH-mutated gliomas, from in vivo and ex vivo experiments, as well in cell cultures, possibly to compensate for the altered flux of alphaketoglutarate to 2HG in IDH-mutated gliomas [START_REF] Nagashima | Diagnostic value of glutamate with 2hydroxyglutarate in magnetic resonance spectroscopy for IDH1 mutant glioma[END_REF][START_REF] Ohka | Quantitative metabolome analysis profiles activation of glutaminolysis in glioma with IDH1 mutation[END_REF][START_REF] Reitman | Profiling the effects of isocitrate dehydrogenase 1 and 2 mutations on the cellular metabolome[END_REF][START_REF] Lenting | Isocitrate dehydrogenase 1-mutated human gliomas depend on lactate and glutamate to alleviate metabolic stress[END_REF] .

Serine was shown, by mass spectrometry experiments, to be significantly lower in IDHmutated, 1p/19q codeleted gliomas, compared to non-codeleted gliomas, and to strongly correlate with cystathionine concentrations [START_REF] Branzoli | Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy[END_REF] . Both serine and cystathionine are involved in glutathione biosynthesis pathways and were suggested to play crucial roles in the compensatory antioxidant mechanisms in gliomas with 1p/19q codeletion [START_REF] Branzoli | Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy[END_REF][START_REF] Harris | Glutathione and Thioredoxin Antioxidant Pathways Synergize to Drive Cancer Initiation and Progression[END_REF][START_REF] Takano | Decreased Expression of Cystathionine β-Synthase Promotes Glioma Tumorigenesis[END_REF] .

The quantification of the full metabolic profile is crucial for the characterization of brain tumors. In vivo MR spectroscopy may help in understanding the role of several metabolites in different tumor subtypes noninvasively. The accurate quantification of neurochemical profiles can be extremely useful for tumor diagnosis, prognosis, and monitoring of anti-cancer treatments in vivo. The omission from the spectral analysis of newly discovered metabolites such as cystathionine may lead to severe biases in the quantification of other key neurochemicals and may prevent from the understanding of their role in cancer metabolism.

Figure captions

Figure 1 .

 1 Figure 1. In vivo 1 H MEGA-PRESS and optimized PRESS spectra. (A) The in vivo MEGA-PRESS spectrum acquired at 3 T in one subject with an IDH1-mutated, 1p/19q codeleted glioma is shown together with the LCModel cystathionine contribution. The cystathionine pattern is visible at 2.72 ppm with no overlap with other metabolites. A linebroadening of 1 Hz was applied to the in vivo spectrum for display purposes only. (B) The in vivo optimized PRESS spectrum acquired for the same subject is shown together with the in vitro cystathionine spectrum, LCModel fits obtained with and without cystathionine in the basis set, the cystathionine, aspartate, betaine, citrate, GABA and serine contributions, the baselines and the residuals resulting from the two fits. No line-broadening was applied to in vivo data. The VOI is shown on a FLAIR image. A gaussian line-broadening of 0.2 s was applied to the cystathionine phantom spectrum. (C) The in vivo optimized PRESS spectrum acquired in the normal brain tissue of the same subject is shown together with the LCModel fit obtained with the basis set with all metabolites, the cystathionine contribution, the baseline and the residuals. The VOI is shown on a FLAIR image.

Figure 2 .

 2 Figure 2. Correlation between cystathionine quantified with PRESS and MEGA-PRESS. Regression plot of cystathionine concentrations measured in tumor tissue by optimized PRESS and MEGA-PRESS in vivo. Cystathionine concentrations obtained from PRESS show a significant correlation with those derived by MEGA-PRESS (r = 0.58; p = 0.004).

Figure 3 .

 3 Figure 3. Effect of cystathionine on metabolite quantification from optimized PRESS spectra. Box-plots of (A) metabolite concentrations and (B) CRLBs obtained with and without cystathionine in the LCModel basis set. Only metabolites showing concentration differences that are statistically significant and greater than 10% between the two fitting approaches are shown. For each box, the central mark indicates the median concentration and the bottom and top edges indicate the 25th and 75th percentiles, respectively. Circles represent values from individual subjects. Asp: aspartate; Bet: betaine; Cit: citrate; Cth: cystathionine; GABA: γ-aminobutyric acid; Ser: serine.

Table 1 . Metabolite concentrations obtained with and without cystathionine in the LCModel fit.

 1 Concentrations are reported as mean value  standard deviation. Cramér-Rao Lower Bound (CRLB) are reported in mM. If the concentration was zero for a given metabolite and subject, the corresponding CRLB (999%) was not considered in the calculation. # indicates the number of subjects (out of 23) for which each metabolite was detected with a CRLB < 50%. Statistical significance was set for p < 0.0028. Bold characters highlight metabolites showing concentration differences that are statistically significant and greater than 10% between the two fitting approaches.

		Fit with all metabolites	Fit without cystathionine	Ratio	P value
		Concentration	CRLB	#	Concentration	CRLB	#
		(mM)	(mM)		(mM)	(mM)
	Asc	1.31  0.56	0.28	22	1.32  0.51	0.31	22	1.01	0.80
	Asp	1.

33  1.14 0.44 14 3.59  1.21 0.40 23 2.69 310 -11 Bet 0.24  0.16 0.05 20 0.28  0.19 0.05 20 1.11 110 -4

	GSH	0.86  0.47	0.14	21	0.83  0.42	0.15	21	0.99	0.12
	mIns	6.99  2.22	0.30	23	6.97  2.24	0.31	23	1.00	0.46
	Lac	5.29  2.82	0.31	23	5.35  2.85	0.34	23	0.97	0.03
	Ser	2.08  1.59	0.67	16	1.51  1.88	0.71	10	0.70	710 -4
	sIns	0.43  0.24	0.04	21	0.44  0.24	0.05	21	1.01	210 -5
	Suc	0.17  0.09	0.07	14	0.17  0.08	0.08	10	0.99	0.17
	Tau	1.02  0.63	0.25	19	1.08  0.46	0.27	20	1.07	310 -5
	tCho	3.98  2.19	0.05	23	3.95  2.17	0.05	23	0.98	0.06
	tCr	7.91  2.08	0.13	23	7.79  2.06	0.15	23	0.99	710 -8
	tNAA	5.29  1.76	0.11	23	5.39  1.77	0.11	23	1.03	210 -11
	Cit	0.74  0.65	0.16	20	1.13  0.72	0.17	23	1.50	410 -8
	Cth	2.20  0.80	0.27	23
	2HG	3.43  2.11	0.38	23	3.65  2.98	0.41	23	1.09	110 -5
	GABA	0.59  0.72	0.24	11	0.80  0.78	0.26	14	1.39	410 -7
	Gln	3.87  1.67	0.31	23	4.20  1.72	0.34	23	1.09	710 -8
	Glu	4.00  1.55	0.32	23	4.26  1.62	0.33	23	1.08	510 -8

2HG: 2-hydroxyglutarate; Asc: ascorbate; Asp: aspartate; Bet: betaine; Cit: citrate; Cth: cystathionine; GABA: γ-aminobutyric acid; Gln: glutamine; Glu: glutamate; GSH: glutathione; mIns: myo-inositol; Lac: lactate; Ser: serine; sIns: scyllo-inositol; Suc: succinate; Tau: taurine; tCho: total choline; tCr: total creatine; tNAA: N-acetylaspartate + N-acetylaspartyl-glutamate.

Acknowledgements

The authors would like to thank Edward J. Auerbach, Ph.D. for implementing MRS sequences on the Siemens platform. FB and SL acknowledge support from Investissements d'avenir [grant number ANR-10-IAIHU-06 and ANR-11-INBS-0006]. FB acknowledges support from Agence Nationale de la Recherche [grant number ANR-20-CE17-0002-01]. DD and MM acknowledge support from following National Institutes of Health grants: BTRC P41 EB015894 and P30 NS076408. MS acknowledge support from INCa-DGOS-Inserm_12560 (SiRIC CURAMUS).


C Celle

C Suspène

M Ternisien

S Lenfant

David Guérin

K Smaali

K Lmimouni

J P Simonato

Dominique Vuillaume
email: dominique.vuillaume@iemn.univ-lille1.fr

Interface Dipole : Effects on Threshold Voltage and Mobility for both Amorphous and Poly-crystalline Organic Field Effect Transistors

Keywords: Organic transistor, organic monolayer, dipole

Interface dipole : effects on threshold voltage and mobility for both amorphous and poly-crystalline organic field effect transistors

Introduction

The control of the threshold voltage (VT) of organic field-effect transistors (OFET) is still a key problem. Many groups have reported that the intercalation of a self-assembled monolayer (SAM) of molecules bearing a dipole between the gate dielectric and the organic semiconducting (OSC) film is an efficient way to modulate VT over a large range of values (more than few tens of Volt). [1][2][START_REF] Chung | Controlling electric dipoles in nanodielectrics and its applications for enabling air-stable n-channel organic transistors[END_REF][START_REF] Bruening | Polar ligand adsorption controls semiconductor surface potentials[END_REF][START_REF] Pernstich | Threshold voltage shift in organic field effect transistors by dipole monolayers on the gate insulator[END_REF][6][7][8][START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF][START_REF] Gholamrezaie | Charge Trapping by Self-Assembled Monolayers as the Origin of the Threshold Voltage Shift in Organic Field-Effect Transistors[END_REF][START_REF] Ojima | Control of field-effect transistor threshold voltages by insertion of self-assembled monolayers[END_REF] However, other OFET parameters (charge carrier mobility, on/off current ratio) can also be impacted by the SAMs due to their effects on (i) changes in the molecular orientation in the OSC, (ii) neutralization of surface defects, (iii) modification of surface roughness, interface dipole and surface energy. In most cases, a combination of these effects is likely, some of them being certainly dependent on both the dielectrics and the OSCs. As a consequence, several transistors parameters are modified simultaneously, and this feature prevents a clear understanding of the effect of the SAMs. We have previously demonstrated [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] that, in the case of an amorphous OSC, we can tune the threshold voltage alone, while keeping nearly unchanged the other electrical properties (hole carrier mobility, on/off ratio, subthreshold swing). Moreover, it is also difficult to precisely and quantitatively compare the results reported by various groups since, gate dielectric materials, OFET geometries, source and drain technologies are not systematically similar. For example, while a significant effect of SAMs on VT has been reported in the literature, experimental results on the effect of the SAMs on the charge carrier mobility are sparse or not discussed with respect to the dipole moment of molecules. 6,8,[START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] Here, we report on a detailed comparison on the effects of dipolar SAMs on VT and charge carrier mobility for both amorphous (polytriarylamine, PTAA) and polycrystalline (pentacene, P5) OFET made on the same SAMs and the same transistor base substrates (same gate dielectric and source-drain electrode technology and geometry). We show that the same VT shifts are induced by the SAMs (10 different molecules with dipole moments ranging from -2D to 7D) for both PTAA and P5 OFETs. In particular, we observe that a linear correlation is obtained between VT and the dipole-induced charge in the SAM (QSAM) instead of the net dipole moment of the molecule. We also observe that the dipole moments of the molecules used in the SAMs have no significant effect on the charge carrier mobility.

Materials and Methods.

Synthesis, monolayers, device fabrication.

We used 10 molecules for SAM fabrication on the SiO2 gate dielectric (Fig. 1). These molecules have dipole moments (along their long axis) ranging between ca. -2 D and 7 D (see Table 1, and calculation details below).

Self-assembled monolayers.

Synthesis and characterization of the SAMs prepared from PFTS, MPTS, APTS, TAATS, Py and PhC10 were described in our previous publications. [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF][START_REF] Lenfant | Electron transport through rectifying self-assembled monolayer diodes on silicon: Fermi-level pinning at the molecule-metal interface[END_REF] In brief, n + -doped silicon wafers were freshly cleaned and oxidized to provide a dense array of silanol groups (≡Si-OH), which are the anchoring sites for the organosilane molecules. Substrates were first cleaned by sonication in acetone, isopropanol then dichloromethane for 5 min. Wafers were dried under nitrogen flow then they were dipped into a freshly prepared piranha solution (H2SO4-H2O2 2:1 v/v) at 100 °C for 30 min, or submitted to an oxygen plasma treatment (20 mTorr, 10 sccm O2, 10 W, 300 s). They were rinsed thoroughly with deionized water then were dried under nitrogen stream. Caution: the piranha solution reacts violently with organic chemicals. Consequently, it should be handled with extreme care. The silanization reactions were carried out at room temperature in a nitrogen glovebox (<1 ppm H2O and O2). For tBuPh, tBuPhNH2, and tBuPhNO2 SAMs, the freshly cleaned oxidized silicon substrates were immersed in a 10 -3 M solution of the corresponding organosilane in anhydrous toluene then samples were kept in the dark for 4 days. Concerning PhC4 SAM, the freshly cleaned silicon substrates were immersed for 2 h in a 10 -3 M solution of PhC4 in a mixture of hexane-dichloromethane 70:30 v/v. Functionalized substrates were cleaned in dichloromethane then isopropanol by sonication then blown with dry nitrogen.

OFET

The OFETs (both PTAA and P5) were processed using a bottom gate/bottom contact configuration. We used n + -silicon (resistivity 1-3mΩ.cm) covered with a thermally grown 220 nm thick silicon dioxide (135 min at 1100°C in presence of oxygen 2L/min followed by a post-oxidation annealing at 900°C in N2 2L/min). The source and drain electrodes were patterned by optical lithography. Titanium/gold, (3/70 nm) were deposited by vacuum evaporation and lift-off. We fabricated linear and interdigitated OFETs with channel lengths L =1, 2, 5, 10, 20 and 50 µm and channel width W = 1,000 µm (linear OFET) and L= 2, 5, 10, 20 and 50 µm and W = 10,000 µm (interdigitated OFET). The back side of the silicon wafer was metalized by 200 nm thick aluminum to form an ohmic contact with the transistor gate. A 1wt% solution of PTAA (S1000 from Merck) in toluene was spin-coated on the substrates. Devices were annealed at 100°C for 20 min leading to 50 nm thick uniform thin films. Pentacene (P5 99%, purified 5 times, from Polysis) was vacuum sublimated at 0.01 nm/s with a nominal thickness of 30 nm).

Film characterization.

Spectroscopic Ellipsometry: We recorded spectroscopic ellipsometry data in the visible range using an UVISEL (Jobin Yvon Horiba) spectroscopic ellipsometer equipped with a DeltaPsi 2 data analysis software. The system acquired a spectrum ranging from 2 to 4.5 eV (corresponding to 300-750 nm) with intervals of 0.1 eV (or 15 nm). Data were taken at an angle of incidence of 70°, and the compensator was set at 45.0°. We fitted the data by a regression analysis to a film-on-substrate model as described by their thickness and their complex refractive indexes. Due to thickness variations of the thermally grown silicon dioxide, we used two samples during the SAM formation process: a thermally grown 200 nm thick silicon dioxide with the electrodes and a second silicon sample with a native oxide (1.0 to 1.5 nm thick). The SAM thickness was measured only on this second sample by spectroscopic ellipsometer. We assume that the SAM thicknesses measured on this second sample and on the sample with the thermally grown silicon dioxide are the same. To determine the monolayer thickness, we used the optical properties of silicon and silicon oxide from the software library, and for the monolayer we used the refractive index of 1.50. The usual values in the literature are in the range of 1.45-1.50. [START_REF] Parikh | An intrinsic relationship between molecular structure in self-assembled n-alkylsiloxane monolayers and deposition temperature[END_REF][START_REF] Ulman | An introduction to ultrathin organic films : from Langmuir-Blodgett to Selfassembly[END_REF] As a function of the wafer used, the native oxide layer thickness was measured between 10 and 15Å. The SiO2 thickness was assumed to be unchanged after the monolayer assembly on the surface. We estimated the accuracy of the SAM thickness measurements at ± 1.5 Å. All measured thicknesses are in agreement with the calculated length of the molecules; meaning that we formed reasonably well-packed SAMs for all these 10 molecules.

Contact Angle measurements:

We measured the static water contact angle with a remotecomputer controlled goniometer system (DIGIDROP by GBX, France). We deposited a drop (10-30 μL) of deionized water (18 MΩ.cm -1 ) on the surface, and the projected image was acquired and stored by the computer. Contact angles were extracted by contrast contour image analysis software. These angles were determined a few seconds after application of the drop. These measurements were carried out in a clean room (ISO 6) where the relative humidity (50%) and the temperature (22 °C) are controlled. The precision with these measurements are ±2°. All measured values (Table 1) are in agreement with literature data. [START_REF] Ulman | An introduction to ultrathin organic films : from Langmuir-Blodgett to Selfassembly[END_REF][START_REF] Schreiber | Structure and growth of self-assembling monolayers[END_REF] For the 3 molecules ended by a tertiobutyl group, note that depending on the substituents of the aromatic ring, the hydrophobic behavior of monolayers decreased following the sequence: tBuPh > tBuPhNO2 > tBuPhNH2 (Table 1).

One can observe that the buried polar substituents (NO2 and NH2) influence the surface energy of the monolayers despite the presence of the hydrophobic bulky tertiobutyl end group. Conformation of the molecules within the monolayers can explain this influence.

Moreover steric hindrance of tertiobutyl probably decreases the compactness of the three SAMs.

Kelvin Probe Force Microscopy:

For KPFM experiments we evaporated, on the SAM samples, clean platinum dots through a shadow mask (100 nm thick, with 100 x 100 µm dimensions), and samples were immediately transferred to the KPFM set-up and measured together. This protocol insures that the work function (WF) of the organic monolayer grafted on oxidized silicon is referenced to the same reference electrode (bare Pt dots). Since it is well known that metal atoms easily diffuse through organic monolayers and form many short-circuits, the WF of the upper Pt dots is thus a correct reference for the underlying silicon oxide substrate. KPFM measurements were carried out at room temperature with a Dimension 3100 placed under dry nitrogen atmosphere.

We used Pt/Ir (0.95/0.05) metal-plated cantilevers with spring constant of ca. 3 N/m and a resonance frequency of ca. 70 kHz. Topography and KPFM data were recorded using a standard two-pass procedure, in which each topography line acquired in tapping mode is followed by the acquisition of KPFM data in a lift mode, with the tip scanned at a distance z ~ 80 nm above the sample so as to discard short range surface forces and be only sensitive to electrostatic forces. DC and AC biases (VDC + VAC sin (ωt)) are applied to the cantilever, and the platinum dot is electrically grounded (VAC=2V, ω/2π=70kHz).

Experimentally, the contact potential difference (CPD) is measured using a feedback loop which sets to zero the cantilever oscillation amplitude at ω by adjusting the tip DC bias VDC. This potential is simply equal to the CPD, in absence of side capacitances. [START_REF] Brunel | Determination of the electrostatic lever arm of carbon nanotube field effect transistors using Kelvin force microscopy[END_REF] AFM: The surface morphology of the organic layer was determined by imaging with a Veeco Dimension 3100 atomic force microscope (AFM) in tapping mode. Silicon cantilevers from MikroMasch (NSC15/ALBS) were used to acquire AFM images of 5 x 5 µm² at 1Hz with a resolution of 512 x 512 pixels.

Dipole Calculation:

We calculated the dipole moment using MOPAC included in Chem3D 10.0 software (from Cambridge Soft Corporation, Cambridge, UK, 1996), with the semi-empirical method PM3. First, after an optimized conformation of the molecules (energy minimum optimization performed with MOPAC), the dipole values were calculated for isolated molecules with a CH3 group at the end of the alkyl chain instead of the grafting part of the molecule (-Si-(OMe)3, -Si-(OEt)3, -Si-Cl3). We considered that the leaving groups of the silane derivatives (OMe, OEt, Cl) are not present after the grafting reaction, and, therefore, no longer contribute to the molecular dipole. In the SAMs, the silyl groups form siloxane bonds, which are considered as part of the underline SiO2 substrate. From these calculations on each individual molecule, the dipole values presented in table 1 are the projection of the dipole vector on the main axe of the molecule (this axe is defined by the alkyl part of the molecule in all-trans configuration).

Transistor characterization.

Electrical characterization of the OFETs were performed with an Agilent 4156B semiconductor parameter analyser. For the ID-VG characteristics in the saturation regime, the back and forth gate voltage VG sweeps were carried out from 50V to -60V (40V to -40V for P5), with a step of 1V and at a drain bias of -60V (for PTAA) or -40V (for P5).

Threshold voltage and charge carrier mobility were extrapolated, as usual, from a linear fit of the ID 1/2 -VG plots. For each SAM, we fabricated several batches (max 3) of both PTAA and P5 OFETs and we systematically measured between 10 and 30 devices for each batch. In the following the reported VT and mobility values are the averaged values from all measured devices in each case.

Results and Discussion.

Threshold voltage.

OFETs made of PTAA and P5 with these 10 SAMs (see Fig. 1) were electrically characterized and the threshold voltage, VT, extracted in the saturation regime. Fig. 2 shows typical drain current -gate voltage (ID-VG) and square-root of drain current vs. gate voltage (ID 1/2 -VG) curves for these devices. Following our previous approach [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] we plot in figure 3 the threshold voltage versus the dipole-induced charges in the SAM, QSAM, or more conveniently the surface density of charges QSAM/e, where e is the electron charge. For this set of data, QSAM is calculated according to the Helmholtz equation which relates the potential drop, VSAM, and the charge, QSAM, across a polar monolayer to the gas phase dipole moment, P, as:

V SAM = NP z ε 0 ε SAM (1)
and

Q SAM = C SAM V SAM = NP z t SAM ( 2
)
where N is the surface density of molecules in the SAM, Pz is the dipole moment perpendicular to the substrate, ε0 is the vacuum dielectric permittivity, εSAM is the relative permittivity of the SAM, tSAM is the SAM thickness and CSAM = ε0 εSAM / tSAM is the capacitance of the SAM. As in our previous work, [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] we chose an average value of 2.5x10 14 cm -2 (± 1x10 14 cm -2 ) for N, assuming a reasonable molecule packing in the SAMs (all other values for Pz -calculated -and tSAM -ellipsometry measurements -are given in table 1). If we except the case of the tBuPhNO2 SAM (which will be discussed later), the main conclusion here is that the linear relationship already observed for amorphous PTAA in Ref. [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] also holds for poly-crystalline P5 OFET made on the same Si/SiO2 gate and Au source-drain structure. Solid lines in Fig. 3 are guide for eyes delimiting the lower and upper limits of the estimated correlation zone in this plot. superimposed for tBuPh, see table 1).

Considering the hybrid gate dielectric (SAM on top of SiO2, two capacitors in series), the relationship between the threshold voltage shift and the charge induced by the SAM can be written as

ΔV T = - C SAM + C ox C SAM C ox Q SAM ≈ - Q SAM C ox (3)
where CSAM = ε0 εSAM / tSAM, the capacitance of the SAM (ranging from 1.3-3.1x10 -6 F/cm 2 depending on the SAM thickness, see table 1, and assuming εSAM=2. 5) is larger than Cox = 1.7x10 -8 F/cm 2 (for the typical oxide gate dielectric of 220 nm). Thus, we get ΔVT ≈ -9.4x10 -12 QSAM. From Fig. 3-a, the slope (solid line) is about 2.7x10 -12 V.cm 2 , a value smaller by a factor about 3.5. Many reasons can explain this difference. i) It is possible that the values of QSAM are overestimated. The value of the molecular density, N in Eq. ( 2),

is not exactly known in our case, and may be lowered if we have more disordered SAM.

ii) The calculated values of PZ (table 1) are obviously overestimated. We do not take into account the depolarization effect, which is well known to significantly reduce the molecular dipole in a SAM (by a factor 3 -10) [START_REF] Krzeminski | Theory of electrical rectification in a molecular monolayer[END_REF][START_REF] Natan | Electrostatic properties of ideal and non-ideal polar organic monolayers: implications for electronic devices[END_REF][START_REF] Cornil | Depolarization Effects in Self-Assembled Monolayers: A Quantum-Chemical Insight[END_REF] compared to the value calculated for a single molecule in vacuum (the greater the dipole, the greater the depolarization effect).

iii) In Eq. 3, we used an average value for εSAM (=2. 5), which is again a simplification since the exact value depends on the dipole moment of a given molecule. All these approximations lead to large error bars for QSAM in Fig. 3.

The OFETs with the tBuPhNO2 SAMs showed a significant deviation from the VT-QSAM correlation, VT being more positive than expected from the dipole-based calculated QSAM.

This feature can be attributed to fixed negative charge trapped at the SAM/OSC interface [START_REF] Gholamrezaie | Charge Trapping by Self-Assembled Monolayers as the Origin of the Threshold Voltage Shift in Organic Field-Effect Transistors[END_REF] since NO2 is a good electron acceptor group. . Calculated with εSAM=2.5 6. protonated at 10% (see our previous paper [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] ) 7. cationic at 50% (see our previous paper [START_REF] Celle | Selfassembled monolayers for electrode fabrication and efficient threshold voltage control of organic transistors with amorphous semiconductor layer[END_REF] ) n.m. : not measured

Monolayer voltage.

For some SAMs (tBuPh, tBuPhNO2 and tBuPhNH2), to obtain a better estimate of QSAM, we measured the monolayer voltage VSAM by Kelvin Probe Force Microscopy (KPFM). Fig. 4 shows a typical KPFM image and the KPFM histograms (see section 2 Materials and Methods for details) for these 3 SAMs compared to the bare SiO2 surface, serving as reference. When the tip is over the Pt dots, the measured CPD is around zero (the Pt/Ir tip and Pt dots have almost the same WF). When the tip moves over the organic monolayer, we clearly observe a significant increase of the measured CPD, corresponding to a reduction of the WF of the functionalized substrate. Results in Fig. 4 clearly indicate that the WF of functionalized silicon oxidized is reduced by ca. 510mV, 340mV and 220mV for the tBuPh, tBuPhNH2 and tBuPhNO2 SAMs, respectively, compared to the bare Pt (we calculate the WF change from the maximum of the histogram peak). The corresponding VSAM values (the difference between CPD with the SAMs and the CPD of SiO2) are 85 mV (tBuPh), -230 mV (tBuPhNO2) and -115 mV (tBuPhNH2). The related QSAM values (calculated from Eq. 2) are given in Table 1. Except for the tBuPh SAM (for which QSAM evaluated from the calculated dipole and by KPFM are almost the same, see Table 1), we note a shift of QSAM measured by KPFM towards negative values (with respect to the calculated ones) for the tBuPhNO2 and tBuPhNH2 SAMs. This shift may be explained by one or more of the previously discussed features (dipole overestimation, trapped charges at the OSC/SAM interface). The data for these two SAMs are re-plotted as open symbols in Note that this KPFM evaluation on "naked" SAMs in air (no OSC on the top) are not accurately representative of the "full" device since charge trapping by the SAMs at the

SAM Pt SAM Pt

SAM/OSC interface is likely contributing to the threshold voltage shift as shown by Gholamrezaie at al. [START_REF] Gholamrezaie | Charge Trapping by Self-Assembled Monolayers as the Origin of the Threshold Voltage Shift in Organic Field-Effect Transistors[END_REF] Moreover, all our devices showed hysteresis in their ID-VG curves of the order of 5 to15 V (Fig. 2). This hysteresis behavior can be due to trapping and detrapping of charges at the SAM/OSC interface with a dynamic on the same time scale as the voltage sweep. The observed hysteresis voltage value corresponds to charge trapping, QT = VHCox (with VH the amplitude of the I-V hysteresis), in the range 0.5-1.5 x 10 12 cm -2 . This value is small compared to the large error on QSAM (as explained above)

and is neglected here (the VT values for the backward and forward traces of the I-V curves were not distinguished in the data analysis and averaged together). Note that the I-V hysteresis on the reference sample (SiO2/OSC interface, no SAM) is generally smaller (< 5

V), thus a large part of these trapping arises at the SAM/OSC interface as shown in ref. [START_REF] Gholamrezaie | Charge Trapping by Self-Assembled Monolayers as the Origin of the Threshold Voltage Shift in Organic Field-Effect Transistors[END_REF] .

Charge carrier mobility.

Charge carrier mobilities extracted in the linear regime are plotted versus QSAM in Fig. 5.

Fig. 5. Charge carrier mobility for PTAA (red circles) and P5 (blue square) OFETs versus the dipole-induced surface density of charges in the SAMs QSAM/e (where e is the electron charge).

For PTAA OFET, we do not observe any significant dependance of the charge carrier mobility with the dipole of the molecules in the SAM. Data are almost constant within a factor ∼ 5, a reasonable dispersion for organic transistors. For P5 OFET, we observe a larger dispersion (a factor ∼ 100), with depends on both the nature of the SAM, but also within different batches of devices for the same SAM (see data for tBuPh, tBuPhNO2 and tBuPhNH2, linked by a thin vertical line). The 10 SAMs used in this work have different surface tension (see water contact angle in Table 1), and it is known that this parameter is one of the parameters that can affect the organization/order in the P5 film. We measured by AFM the size of grain domains of the P5 film between source and drain for the OFET with these different SAMs. Figure 6 shows typical AFM images of P5 films evaporated on tBuPh, tBuPhNO2 and tBuPhNH2 SAMs and on directly SiO2. Data for other SAMs were already published. [START_REF] Ternisien | Contribution à l'étude des transistors à effet de champ organiques : Effet de la fonctionnalisation du diélectrique de grille par des monocouches auto-assemblées[END_REF] The P5 average grain sizes were determined by 2D-FFT analysis using the WsXM software. [START_REF] Horcas | WSXM: A software for scanning probe microscopy and a tool for nanotechnology[END_REF] Fig. 7 shows a correlation plot of the mobility vs. the average P5 grain size.

SiO2 tBuPh tBuPhNH2 tBuPhNO2

Fig. 6. TM-AFM images of P5 films deposited on SAMs and bare SiO2

Fig. 7. Correlation between the charge carrier mobility in P5 OFET with various SAMs (and naked SiO2 as reference) versus the average grain size measured by AFM as shown

in Fig. 6.

While we note a large dispersion in the mobility data and P5 grain size for several P5 deposition batches (labeled R1, R2 and R3) for a given SAM of the same chemical nature, we observe a general trend from this plot. As expected, the grain size is the main factor governing the charge carrier mobility inP5 OFET. Thus, we can conclude that the dipole moment of the SAMs has no significant influence on the charge carrier mobility, or is hidden by other factors, in particular here the correlation with changes in the structure properties of the organic film (grain size for P5). For the PTAA film, with an amorphous structure, mobility is roughly constant whatever the nature of the SAMs.

Conclusion.

In E-mail : jean-pierre.simonato@cea.fr; dominique.vuillaume@iemn.univ-lille1.fr 217 mL (1.23 mmol) of 4-tert-butylbenzylamine were diluted in 15 mL of chloroform in a 50 mL round-bottom flask equipped with a cooler and a magnetic stirring bar. The vessel was sealed with a rubber septum, evacuated and backfilled with argon. 267 µL (1.08 mmol) of (3isocyanatopropyl)triethoxysilane were then rapidly added with a syringe and the mixture was heated with stirring at reflux under argon for 20 h. After cooling down to room temperature, the solvent was removed in vacuum to afford the desired compound as a pale yellow oil (440 mg, yield: 87 %). It was possible to purify the silane by flash chromatography on SiO 2 column using a mixture of hexane-AcOEt 50:50 v/v as eluent (white solid, 250 mg, yield: 50% after chromatography). tBuPh was stored under nitrogen.

Supplementary Materials 1-Synthesis of molecular precursors for the fabrication of SAMs

Synthesis of tBuPhNO

2 : 1-(4-tert-butyl-2,6-dinitrobenzyl)-3-(3-(triethoxysilyl)propyl)urea
This synthesis is performed in 6 steps:

Each step is described in the following section.

4-tert-butyl-2,6-dinitrobenzoic acid

24 mL of sulfuric acid (100 %) were slowly added with stirring at 0 °C to 18 mL of nitric acid (100 %) placed in a 100 mL round-bottom flask. In parallel, 4.3 mL (24.4 mmol) of 4-tertbutylbenzyl alcohol were placed in another 100 mL round-bottom flask. The mixture of fuming acids was then slowly added to the alcohol at 0 °C. The resulting solution was stirred at room temperature for 2 h and at 80 °C for 3 h and was then poured into 200 mL of iced water. The precipitate was filtered, copiously rinsed with water and dried under vacuum to afford the desired compound as a yellow solid (5.2 g, yield: 77 %). (4-tert-butyl-2,6-dinitrophenyl)methanol 5.2 g (19.4 mmol) of 4-tert-butyl-2,6-dinitrobenzoic acid were dissolved in 20 mL of dichloromethane in a 500 mL round-bottom flask. Then, 2.7 mL (19.4 mmol) of triethylamine and 2.5 mL (19.4 mmol) of trimethylsilyl chloride were successively added with stirring at 0 °C. The mixture was allowed to warm to room temperature and was stirred for 3 h. In parallel, 58.6 mL (58.2 mmol) of a solution of diisobutylaluminium hydride (1 M in tetrahydrofuran) were added dropwise to 20 mL of tetrahydrofuran at 0 °C in another 500 mL round-bottom flask previously evacuated and backfilled with argon. The solution containing the trimethylsilyl-protected acid was then slowly added to the solution of diisobutylaluminium hydride in tetrahydrofuran at 0 °C. The resulting mixture was allowed to warm to room temperature and was stirred for 24 h under argon. Another equivalent (58.6 mL) of a solution of diisobutylaluminium hydride (1 M in tetrahydrofuran) was next introduced and the solution was stirred at room temperature for 48 h under argon. The reaction was quenched by addition of 50 mL of methanol and solvents were removed under vacuum. The residue was redissolved in 20 mL of methanol and solid impurities were filtered off. 200 mL of a saturated solution of ammonium chloride were then added and the organic phase was extracted three times with 25 mL of dichloromethane and filtered over Celite®. Drying over magnesium sulfate, filtration and evaporation of solvents afforded the desired compound as a red-brown solid (4.5 g, yield: 91 %).

5-tert-butyl-2-(chloromethyl)-1,3-dinitrobenzene

4.5 g (17.7 mmol) of (4-tert-butyl-2,6-dinitrophenyl)methanol were dissolved in 20 mL of dichloromethane in a 100 mL round-bottom flask. 1.6 mL (21.2 mmol) of thionyl chloride were then added and the mixture was stirred at room temperature for 4 h. The excess of thionyl chloride and the solvent were removed in vacuo to afford a red-brown solid (4.5 g, yield: 93 %). 2-(4-tert-butyl-2,6-dinitrobenzyl)isoindoline-1,3-dione 2.5 g (9.2 mmol) of 5-tert-butyl-2-(chloromethyl)-1,3-dinitrobenzene were dissolved in 20 mL of dimethylformamide in a 100 mL round-bottom flask. 1.9 g (10.2 mmol) of phthalimide potassium salt were then added and the mixture was stirred at 80 °C for 24 h. The resulting solution was poured into 150 mL of water and the organic phase was extracted three times with 20 mL of dichloromethane, intensively rinsed with water and dried over magnesium sulfate. Filtration and evaporation of the solvent afforded the desired compound as an orange solid (1.7 g, yield: 49 %) (4-tert-butyl-2,6-dinitrophenyl)methanamine 1.2 g (3.1 mmol) of 2-(4-tert-butyl-2,6-dinitrobenzyl)isoindoline-1,3-dione were dissolved in 20 mL of tetrahydrofuran in a 100 mL round-bottom flask. 761 mL (15.7 mmol) of hydrazine hydrate were then added and the mixture was stirred at 80 °C for 3 h. After cooling down to room temperature, 10 mL of a solution of hydrochloric acid (37 %) were introduced and the mixture was stirred at 80 °C for 1 h. The precipitate was then filtered and copiously rinsed with water. After deprotonation by addition of sodium hydroxide in 30 mL of an ethanol/water 1:1 mixture, the organic phase was extracted three times with 10 mL of dichloromethane and dried over magnesium sulfate. Filtration, evaporation of the solvent and recrystallization from acetone afforded pale yellow crystals (620 mg, yield: 78 %).

Synthesis of tBuPhNO

2 : 1-(4-tert-butyl-2,6-dinitrobenzyl)-3-(3-(triethoxysilyl)propyl)urea
250 mg (0.99 mmol) of (4-tert-butyl-2,6-dinitrophenyl)methanamine were diluted in 10 mL of chloroform in a 50 mL round-bottom flask equipped with a cooler and a magnetic stirring bar. The vessel was sealed with a rubber septum, evacuated and backfilled with argon. 270 µL (1.08 mmol) of (3-isocyanatopropyl)triethoxysilane were then rapidly added with a syringe and the mixture was heated with stirring at reflux under argon for 20 h. After cooling down to room temperature, the solvent was removed in vacuo and the oily residue poured into 20 mL of pure pentane (> 99 %). After sonication and decantation, the supernatant was removed with a syringe and then the residue was dissolved in 5 mL of dichloromethane and transferred into a Schlenk tube with a cannula. The solvent was finally evaporated to afford the desired compound as a brown solid (300 mg, yield: 61 %). For the preparation of SAMs, the silane could be further purified by flash chromatography on SiO 2 using a mixture of CHCl 3 -AcOEt 70:30 v/v as an eluent. A pale brown solid was isolated and stored under nitrogen. 1 H NMR (200 MHz, DMSO, 300 K): δ (ppm) = 0.51 (t, 2H, 3 J = 8.2 Hz, H c ), 1.16 (t, 9H, 3 J = 7.3 Hz, H a ), 1.37 (s, 9H, H o ), 1.61 (p, 2H, 3 J = 8.2 Hz, H d ), 2.95 (dt, 2H, 3 J = 8.2, 5.7 Hz, H e ), 3.71 (q, 6H, 3 J = 7.3 Hz, H b ), 4.22 (d, 2H, 3 J = 5.9 Hz, H i ), 6.16 (t, 1H, 3 J = 5.7 Hz, H f ), 6.51 (t, 1H, 3 J = 5.9 Hz, H h ), 7.67 (s, 2H, H l ).

2-(aminomethyl)-5-tert-butylbenzene-1,3-diamine

200 mg (0.79 mmol) of (4-tert-butyl-2,6-dinitrophenyl)methanamine were dissolved in 15 mL of a solution of hydrochloric acid (37 %) in a 50 mL round-bottom flask. 1.4 g (6.3 mmol) of tin(II) chloride dihydrate dissolved in 10 mL of a solution of hydrochloric acid (37 %) were then slowly added and the mixture was stirred at 70 °C for 15 h. After cooling down to room temperature and deprotonation by addition of sodium hydroxide until pH > 12, the organic phase was extracted three times with 10 mL of dichloromethane and dried over magnesium sulfate. Filtration, evaporation of the solvent and recrystallization from pentane afforded a pale yellow solid (97 mg, yield: 63 %). Caution: the product was sensitive to light and should be stored in the dark under nitrogen. 50 mg (0.26 mmol) of 2-(aminomethyl)-5-tert-butylbenzene-1,3-diamine were diluted in 10 mL of chloroform in a 50 mL round-bottom flask equipped with a cooler and a magnetic stirring bar. The vessel was sealed with a rubber septum, evacuated and backfilled with argon. 52 µL (0.21 mmol) of (3-isocyanatopropyl)triethoxysilane were then rapidly added with a syringe and the mixture was stirred at room temperature under argon for 15 h. The solvent was removed in vacuo and the oily residue poured into 20 mL of pure pentane (> 99 %). After sonication and decantation, the supernatant was removed with a syringe and then the residue was dissolved in 5 mL of dichloromethane and transferred into a Schlenk tube with a cannula. The solvent was finally evaporated. Analysis showed remaining traces of 2-(aminomethyl)-5tert-butylbenzene-1,3-diamine which should not play any role during the grafting reaction. The obtained compound was a dark orange solid (55 mg, yield: 73 %). Caution: the product was sensitive to light and should be stored in the dark under nitrogen.

4 10Fig. 1 :

 41 Fig. 1 : Chemical structures of dipolar molecules used in SAMs.

Fig. 2 :

 2 Fig. 2 : Typical ID-VG and ID 1/2 -VG characteristics for PTAA OFET (a) and P5 OFET (b) (L = 10 μm and W=1,000 μm in both cases) with a tBuPhNO2 SAM.

Fig. 3 :

 3 Fig. 3 : (a) Relationship between threshold voltage VT and dipole-induced surface density of charges in the SAM QSAM/e (where e is the electron charge) for OFETs with the SAMs made of the 10 molecules shown in Fig. 1. Blue squares for P5 and red circles for PTAA. Solid lines are guide for eyes defining a correlation zone. (b) Comparison between datawith QSAM calculated from the molecular dipole (filled symbols) and measured from KPFM (unfilled symbols) for three SAMs : tBuPh, tBuPhNO2 and tBuPhNH2 (the data are superimposed for tBuPh, see table1).

Fig. 3 -Fig. 4 .

 34 Fig. 3-b. The general linear VT -QSAM relationship remains valid, these values being still in

Figure S1 .

 S1 Figure S1. Chemical structures of various molecular precursors for the fabrication of SAMs.

13 C{ 1

 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 7.8 (C c ), 18.2 (C a ), 23.7 (C d ), 31.2 (C o ), 34.5 (C n ), 42.8 (C e ), 44.1 (C i ), 58.3 (C b ), 125.5 (C l ), 127.1 (C k ), 136.3 (C j ), 150.2 (C m ), 158.5 (C g ). IR (ATR, cm -1 ): 3341 (ν N-H), 3062 (ν C-H Ar ), 2968 (ν as C-H), 2927 (ν s C-H), 1631 (ν C=O), 1571 (n C=C Ar ), 1514 (δ N-H), 1271 (ν C-N), 1235 (ν C-C), 1078 (ν as Si-O-C), 956 (ν s Si-O-C).

1 H 8 (

 18 NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 1.41 (s, 9H, H g ), 8.26 (s, 2H, H d ), 15.63 (s, 1H, H a ). 13 C{ 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 29.1 (C g ), 37.2 (C f ), 127.2 (C d ), 131.C b ), 137.8 (C c ), 152.1 (C e ), 163.8 (C a ). IR (ATR, cm -1 ): 3075 (ν C-H Ar ), 2972 (ν as C-H), 2930 (ν s C-H), 2650 (ν O-H), 1712 (ν C=O), 1588 (n C=C Ar ), 1542 (ν NO 2 ), 1360 (ν NO 2 ), 1288 (ν C-O).

1 H

 1 NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 1.48 (s, 9H, H h ), 2.02 (s br , 1H, H a ), 4.72 (s, 2H, H b ), 7.52 (s, 2H, H e ).13 C{ 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 31.3 (C h ), 38.2 (C g ), 62.9 (C b ), 124.6 (C e ), 129.2 (C c ), 142.1 (C d ), 151.1 (C f ). IR (ATR, cm -1 ): 3369 (ν O-H), 3062 (ν C-H Ar ), 2978 (ν as C-H), 2927 (ν s C-H), 1592 (n C=C Ar ), 1538 (ν NO 2 ), 1364 (ν NO 2 ), 1071 (ν C-O).

1 H

 1 NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 1.49 (s, 9H, H g ), 4.54 (s, 2H, H a ), 7.53 (s, 2H, H d ). 13 C{ 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 30.8 (C g ), 37.2 (C f ), 42.6 (C a ), 124.3 (C b ), 126.3 (C d ), 141.6 (C c ), 150.7 (C e ). IR (ATR, cm -1 ): 3065 (ν C-H Ar ), 2973 (ν as C-H), 2925 (ν s C-H), 1589 (n C=C Ar ), 1538 (ν NO 2 ), 1363 (ν NO 2 ).

13 C{ 1

 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 31.3 (C k ), 38.7 (C j ), 43.1 (C e ), 124.3 (C f ), 126.4 (C h ), 127.3 (C b ), 131.4 (C c ), 133.4 (C a ), 141.7 (C g ), 150.9 (C i ), 169.1 (C d ). IR (ATR, cm -1 ): 3072 (ν C-H Ar ), 2975 (ν as C-H), 2926 (ν s C-H), 1774 and 1714 (ν C=O), 1590 (n C=C Ar ), 1539 (ν NO 2 ), 1368 (ν NO 2 ), 1294 (ν C-N).

1 H

 1 NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 1.48 (s, 9H, H h ), 1.63 (s br , 2H, H a ), 3.95 (s, 2H, H b ), 7.51 (s, 2H, H e ).

13 C{ 1

 1 H} NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 31.3 (C h ), 38.2 (C g ), 45.8 (C b ), 124.9 (C e ), 129.5 (C c ), 142.1 (C d ), 151.2 (C f ). IR (ATR, cm -1 ): 3469 (ν N-H), 3375 (ν N-H), 3063 (ν C-H Ar ), 2973 (ν as C-H), 2924 (ν s C-H), 1588 (n C=C Ar ), 1537 (ν NO 2 ), 1497 (δ N-H), 1364 (ν NO 2 ), 1274 (ν C-N).

13 C{ 1

 1 H} NMR (200 MHz, DMSO, 300 K): δ (ppm) = 7.6 (C c ), 18.4 (C a ), 24.5 (C d ), 31.0 (C o ), 34.3 (C n ), 42.9 (C e ), 43.1 (C i ), 58.5 (C b ), 125.2 (C l ), 129.3 (C j ), 142.3 (C k ), 150.8 (C m ), 159.4 (C g ). IR (ATR, cm -1 ): 3356 (ν N-H), 3061 (ν C-H Ar ), 2972 (ν as C-H), 2927 (ν s C-H), 1638 (ν C=O), 1569 (n C=C Ar ), 1539 (ν NO 2 ), 1474 (δ N-H), 1365 (ν NO 2 ), 1251 (ν C-N), 1072 (ν as Si-O-C), 954 (ν s Si-O-C).Synthesis of tBuPhNH2 : 1-(2,6-diamino-4-tert-butylbenzyl)-3-(3-(triethoxysilyl)propyl)ureaThis synthesis was realized in seven steps. The molecule (4-tert-butyl-2,6dinitrophenyl)methanamine is an intermediate for the syntheses of both tBuPhNO 2 and tBuPhNH 2 .

1 H

 1 NMR (200 MHz, DMSO, 300 K): δ (ppm) = 1.35 (s, 9H, H i ), 2.21 (s br , 2H, H a ), 3.63 (s, 2H, H b ), 5.91 (s, 2H, H f ), 6.12 (s, 4H, H d ).

13 C{ 1

 1 H} NMR (200 MHz, DMSO, 300 K): δ (ppm) = 31.3 (C i ), 40.2 (C h ), 42.3 (C b ), 106.9 (C f ), 114.8 (C c ), 145.1 (C e ), 148.3 (C g ). IR (ATR, cm -1 ): 3439 (ν N-H), 3348 (ν N-H), 3320 (ν N-H), 3061 (ν C-H Ar ), 2975 (ν as C-H), 2920 (ν s C-H), 1602 (C=C Ar ), 1476 (δ N-H), 1270 (ν C-N). Synthesis of tBuPhNH 2 : 1-(2,6-diamino-4-tert-butylbenzyl)-3-(3-(triethoxysilyl)propyl)urea

Table 1 :

 1 Main parameters of the SAMs used in this work. Individual molecule dipole (Pz) from MOPAC calculations, molecule length from geometry optimization, SAM thickness measured by ellipsometry, water contact angle, dipole-induced charges in the SAMs calculated from the molecule dipole and from KPFM experiments.

	SAM	molecule dipole PZ 1 (D)	molecule length 2 (nm)	SAM thickess 3 (nm)	Water CA 4 (°)	QSAM/e from PZ (cm -2 ) 5	QSAM/e by KPFM (cm -2 )
	PFTS	-1.80	1.00	0.90	92	-1.5x10 13	n.m.
	MPTS	-1.60	0.77	0.75	65	-1.2x10 13	n.m.
	APTS	0.70 6	0.74	0.71	72	5.0x10 12	n.m.
	TAATS	7.70 7	1.83	n.m.	89	1.1x10 13	n.m.
	Py	0.15	1.85	1.80	74	2.0x10 11	n.m.
	PhC10	-0.10	1.90	1.80	75	-3.0x10 11	n.m.
	PhC4	-0.30	1.20	1.10	85	-8.2x10 12	n.m.
	tBuPh	0.23	1.72	1.68	78	7.0x10 11	7.6x10 11
	tBuPhNO2	2.70	1.73	1.60	70	8.2x10 12	-2.0x10 12
	tBuPhNH2	1.60	1.53	1.49	58	5.3x10 12	-1.0x10 12
	1. PM3 calculations
	2. From geometry optimization PM3
	3. Ellipsometry measurements (±0.15 nm)
	4. ± 2°5

Effects on Threshold Voltage and Mobility for both Amorphous and Poly-crystalline Organic Field Effect Transistors.

  conclusion, we have shown that the threshold voltage of both amorphous and polycrystalline OFETs are similarly controlled by SAMs of dipolar molecules (same relationship), while the charge carrier mobility is not. This relationship is broken in case of a strong charge trapping at the SAM/OSC interface, as exemplified by SAMs of molecules bearing a good electron acceptor groups. Charge carrier mobility is not affected by the dipole in the SAMs, remaining almost constant (within a factor 5) for amorphous OFET,, while for polycrystalline OFET (pentacene) the large variation of charge carrier mobilities (factor about 100) is related to change in the organic film structure (mostly grain size).

	Interface Dipole : C. Celle (1), C. Suspène (1), S. Lenfant (2), D. Guérin (2), K. Smaali (2), M. Ternisien (2) * , K.
	Lmimouni (2), J.P. Simonato (1) & D. Vuillaume (2)
	1)CEA/LITEN/DTNM/LCRE, 17 rue des Martyrs, F-38054 Grenoble, France
	2) IEMN, CNRS & Univ. of Lille, UMR8520, Avenue Poincaré, Villeneuve d'Ascq, F-
	59652cedex, France.

  NMR (200 MHz, CDCl 3 , 300 K): δ (ppm) = 0.65 (t, 2H, 3 J = 8.3 Hz, H c ), 1.24 (t, 9H, 3 J = 7.3 Hz, H a ), 1.35 (s, 9H, H o ), 1.65 (p, 2H, 3 J = 8.3 Hz, H d ), 3.18 (dt, 2H, 3 J = 8.3, 5.8 Hz, H e ), 3.85 (q, 6H, 3 J = 7.3 Hz, H b ), 4.32 (d, 2H, 3 J = 5.9 Hz, H i ), 4.79 (t, 1H, 3 J = 5.8 Hz, H f ), 4.92 (t, 1H, 3 J = 5.9 Hz, H h ), 7.32 (m, 4H, H k + H l ).

1 H

H NMR (200 MHz, DMSO, 300 K): δ (ppm) = 0.50 (t,

2H, 3 J = 8.1 Hz, H c ), 1.15 (t, 9H, 3 J =

Acknowledgements

Part of this work has been financially supported by the ministry of research (PhD grant of M.T.), the Agence National de la Recherche (CADISCOM project and post-doctoral grant for K.S.). The post-doctoral grant for C.S. was supported by the Chimtronique program of CEA.