Nathalie Cheminet

Sandra L Nogueira

Oumayma Benaqqa

Zakaria El Malki

Mohamed Bourass

Simon Cassegrain

Mohammed Hamidi

Mohammed Bouachrine

Jean Marc Sotiropoulos

Karinne Miqueu

Thibaut Jarrosson
email: thibaut.jarrosson@enscm.fr

Claude Niebel

Jean-Pierre Lere-Porte

Francoise Serein-Spirau
email: francoise.spirau@enscm.fr

Jean-Pierre Lère

Françoise Serein-Spirau

Zakaria El

: Estm

Elaboration of low-band-gap -conjugated systems based on thieno[3,4-b]pyrazines

Keywords: π-conjugated systems, low band-gap, organic electronics, POC-17

In order to understand the electronic contribution of the thienopyrazine building block on the electronic properties of π-conjugated systems, small molecules containing thiophene and thienopyrazine moieties are synthesized and jointly studied by theoretical (DFT) and experimental methods (UV-Vis, UPS).Taking advantages of these preliminar attractive results, four low band gap extended structures have been elaborated on the base of Donor-Acceptor-Donor sequences (DAD); these elongated π-conjugated molecules exhibit noticeable electronic and absorption properties spreading from the near UV to NIR regions.

Introduction

Since the 90s, π-conjugated materials have attracted a continuous growth of interest due to their large properties scope that can be useful for a wide range of applications and particularly in organic electronics [START_REF] Grassen | Organic Electronics[END_REF][START_REF] Klauk | Organic Electronics: Materials, Manufacturing, and Applications[END_REF][START_REF] Dou | [END_REF].Their optical and electronic properties are used in sensor devices [START_REF] Shinar | Organic Electronics in Sensors and Biotechnology[END_REF][START_REF] Zhang | [END_REF], in organic electroluminescent diodes [6,7], in thin film organic field effect transistors [8][9][10][11][12][13], in bulk heterojunction organic photovoltaic cells and more recently for near infrared photodetectors [START_REF] Dou | [END_REF][14][15][16][17]. In order to reach better transport properties and to favour the production of technologically useful materials, it appears essential to study the relationships between the chemical structures and electronic and optical properties for this goal. The band gap control remains a decisive parameter.

Conversely, among the large diversity of chemical structures, thieno [3,4-b]pyrazines are well known to be suitable sub-structures to stabilize quinoid resonance moieties. Therefore, they appear to be good candidates to efficiently reduce the band gap [18,19].

Consequently, thieno [3,4-b]pyrazine building blocks are appropriate to enhance planarity of sequences leading to fully conjugated segments. Indeed, N-containing heterocyclic aromatics like thieno [3,4-b]pyrazines are described to behave as relevant electron acceptors; this species are easily tunable and dependant, on the one hand, on the two substituents in the 2 and 5 positions on the thiophene unit and, on the other hand, on the two groups borne on the adjacent carbons to the nitrogen atoms.

This study exhibits the role of the heterocyclic six membered pyrazine condensed on the thiophene ring,relative to the LUMO energy level of small model molecules. A similar process is carried out on slightly longer π-conjugated systems that are finally included in an alternated donor (D)-acceptor (A) sequence in order to build a D-A-D extended conjugated structures.

Results and discussion

Elaboration of parent thienopyrazines 3a-f Synthesis Compounds 3 are prepared by condensing 3,4-diaminothiophene with a 1,2-diketone [20]. In this synthesis, 3,4-diaminothiophene was generated in situ from the ammonium salt 1 using triethylamine as a base (Fig. 1).

To vary the Z substituents, two condensation methods were explored using a wide range of 1,2-diketones. In a first methodology, the 1,2-diketones are condensed in refluxing absolute ethanol (Method 1). In those conditions, an optimal yield is obtained under 3 days refluxing. Alternatively, microwave activation [21] (at 150 °C in absolute ethanol) decreases the reaction time to only 10 min (Method 2). Only four compounds 3a-d are isolated by common extraction methods and purified on silica gel by flash chromatography. Overall, reaction yields vary from low to fair (22-52 %), probably due to the low stability of the deprotonated "diamino" starting material 1. 3e cannot be isolated, because of its poor stability; 3f, bearing an extended flat π system, exhibit a little solubility and is not obtained.

Electronic and optical properties of compounds 3a-d

In a first step, interest was dedicated to better appreciate, in terms of frontier orbitals levels, the role of the pyrazine condensed ring itself on the thiophene moiety to build the thienopyrazine structure: density functional theory (DFT) computational studies were undertaken to evaluate molecular orbitals levels and electronic band-gaps of the systems.

Every compound geometries were first optimized with DFT B3LYP functional and 6-31G** basis set. At the minimum energy level, all the thienopyrazines, here reported, prove a planar structure and confirm an extended conjugation as previously reported [10,11,22]. Precisely, calculated frontier molecular orbitals levels of the thiophene ring exhibit, in these conditions, HOMO and LUMO ones at -6.35 and -0.23 eV, respectively (Fig. 2), giving an electronic gap Eg of 6.12 eV. Referring to thienopyrazines 3a-d HOMO and LUMO levels and band gaps calculations which are reported in Table 3, the introduction of the pyrazine ring fused to the thiophene moiety dramatically decreases their LUMO level and consequently contributes to a significant reduction of the gap Eg inducing an extended conjugation. Moreover, the Z substituents of the pyrazine sub-structure impact the LUMO levels values but no correlation can be easily established with the LUMO level and the band gap value in relation with their significant well-known electronic effects: indeed, geometric parameters due to steric hindrances origin a calculated dihedral bond angle of about 40° between the thienopyrazine units and the Z substituent, confirming the Z substituents role on the band gap tuning.

To validate the electronic properties established by the calculations results, ultraviolet photoelectrons spectroscopy experiments (UPS), in the gas phase, have been carried out on 3a-d compounds. Photoelectrons spectrum of compound 3b given as an example in Fig. 3a gives the ionization potentials exhibiting three bands at 9 eV, about 10 eV and at 11 eV that can be respectively correlated to the HOMO, HOMO-1 and HOMO-2 levels. These experimental data are in accordance with DFT computational studies, enabling to clearly attribute the band at 9 eV to the HOMO orbitals. Moreover, experimental electronic and optical properties of compounds 3a-d are recorded and evidence to value the substituents Z effect on the absorption properties useful to estimate the optical band gap (Table 1). Absorption maxima are in the range of 315-348 nm and emission properties are included in the 450-465 nm region. These experimental data are compared to computational time dependent density functional theory (TD-DFT) analysis, dedicated to predict the absorption spectrum. Even if optical band gap does not stricly correspond to the value of the molecular band gap Eg, the two gap values are informative and can be given and compared. Optical gaps deduced from absorption maxima data are in good agreement with the experimental data (Table 1). Comparing the absorption maxima of the four compounds 3a-d, the lowest one is observed for 3a when the Z substituents are ethyl groups: ethyl group cannot participate or increase any conjugation. Otherwise, when the substituents are phenylene groups, they can participate to the thienopyrazine conjugation and play a more significant effect. Comparing 3a and 3b, a 30 nm bathochromic effect is recorded and this tendency is confirmed when phenylene rings are para substituted by a methoxy or a fluorine group. As expected, the electronic nature of the methoxy (donnor effect) or the fluorine (acceptor effect) clearly influence the electronic properties of compounds 3c and 3d. The phenylene substituent electronic role manages the HOMO and LUMO levels and therefore the electronic gap of the compounds but, surprisingly, does not impact the emission properties.

Elaboration of thiophene disubstituted thienopyrazine compounds 7

A second class, made by adding two thiophene units on the thienopyrazine structure has been elaborated in order to study if the resulting extended structure follows similar electronic behaviours, concerning the determining parameters on their conjugation properties (Fig. 4).

Synthesis

The π-elongated thienopyrazines 7a-f are prepared following literature procedures [23,24]. The synthetic route starts with the quasi quantitative dinitration of 2,5-dibromothiophene under strong acidic condi tions. Then a double Stille cross-coupling reaction is engaged with 2-tributylstannylthiophene and 4 % of PdCl2(PPh3)2 as the pre-catalyst to obtain 5 in good yield (68 %). The reduction of the two nitro groups with iron in glacial acetic acid at 60 °C leads after 16 h to the quantitative formation (95 %) of the diamino corresponding compound 6 which was quickly engaged in the next step because it is air sensitive. As previously described for compounds 3a-d, the condensation of 6 with the appropriate 1,2-diketones 2a-f was realised under regular heating method or under microwave irradiation. Yields of 7a-f elaborations are reported in Table 2 and are better for longer compounds 7a-f (25-65 %) than for 3a-d products: this can be explained by the better solubility of the longer products compared to the small systems. In this series, diketones 2e and 2f can be successfully condensed on the diamino precursor and enabled us to extend our evaluation study.

Electronic and optical properties of compounds 7a-f

The same methodology involved in the 3a-d electronic behaviour understanding, is implemented to study the role of the pyrazine condensed ring on the central thiophene moiety of a terthiophene entity to build the thienopyrazine structure. The same computational conditions previously described are used: calculated frontiers molecular orbitals levels of terthiophene located HOMO and LUMO ones at -6.17 and -1.68 eV, respectively, giving an electronic gap Eg of 3.49 eV. This lower value compared to that of thiophene (6.12 eV) is due to the π-conjugated system lengthening.

Considering thienopyrazine derivatives 7a-f, their LUMO levels and their band gaps Eg are significatively reduced and as it was concluded for compounds 3a-d, this evolution is due to the pyrazine substructure presence in the chemical conjugated segment. UPS spectra are recorded in the gas phase for compounds 7, showing two main bands with a poorer resolution: the comparison of the lower energy band (7eV) of 7b, taken as an example, with that of 3b (9 eV) indicates a noticeable extension of conjugation between the two series of compounds. Deconvolution of the bands enables the identification of some molecular orbitals as shown in Fig. 3b.

From molecular orbital fragments of thienopyrazine 7f, HOMO and LUMO molecular orbitals correlation diagram is presented in Fig. 2. This type of analysis has been studied by A. S. Özen with molecules containing thiophene and thienopyrazine building blocks [19]. It appears that the LUMO of compound 7f comes from almost exclusively the thienopyrazine unit. On the other hand, the HOMO orbital of 7f is a mix between the thiophene and the thienopyrazine units. As shown in Fig. 2, the HOMO is delocalized on all the three thiophene units.

In all compounds, whereas the HOMO levels appear slightly influenced by the thienopyrazine presence in the conjugated sequence, the geometric and the electronic properties of the Z substituents on the pyrazine moiety drastically decrease the LUMO level.

For this purpose, phenylene substituents and para substituted ones are more efficient than ethyl groups; among these aromatic structures biphenyl structure leading to 7f reveals to be remarkably efficient to decrease the LUMO level because it freezes the thienopyrazine core in a rigid and planar morphology, consequently enhancing the conjugation of the segment (Fig. 5). Optical properties of compounds 7a-f indicate absorption maxima in the range of 504-630 nm and emission properties in the 620-725 nm region (Table 2). The experimental results are in accordance with the theoretical calculations tendencies. Conversely, when 3c and 3d optical properties are compared to 7c and 7d absorption properties, the latter are not influenced by the para substituent (methoxy or fluorine) on the phenylene ring. Among these results, experimental optical data confirm 7f interesting extended conjugation brought by the resulting planar phenanthrene substructure fused on the thienopyrazine moiety with an absorption maximum located at 630 nm. This result confirms the importance of planarity on the expansion of the π-system [8].

Comparing the two classes of thienopyrazine derivatives, a huge bathochromic shift of around 200 nm is observed between the parent thienopyrazines 3a-d and the π-elongated thienopyrazines 7a-d and considering the small increase of the π-system, this red shift is relatively important and indicates how much efficient the thienopyrazine moiety is to improve conjugation of a π-conjugated segment.

Elaboration of thienopyrazines based DAD sequences 12

As demonstrated above, thienopyrazine sequences are relevant to elaborate fully conjugated systems and can operate as electron acceptors entities having a rather low LUMO level. In order to develop interesting advanced materials for electronic organics, fully conjugated materials are aspired to include requirements concerning their optical properties up to the near infra-red domain. For this purpose, the band-gap needs to be low and a strategy to decrease the gap, described by Roncali [8], is in a first step, the planarization and the rigidification of the π system by covalent and non-covalent bonding. Secondly, this goal can also be reached by associating π-conjugated donors (D) and π-conjugated acceptors (A) building blocks.

Disubstituted [3,4-b]thienopyrazines described above will be the electron acceptors units and will be combined to two π-conjugated electron donors as [bisthiophene 1,4-bis-(2ethylhexyloxy)phenylene] in order to build DAD sequences [25].

Synthesis

In a first strategy towards the synthesis of the DAD systems 12, a two steps process is envisaged starting with the direct dibromation on the α position of the thiophene units of 7a-d (Fig. 6). By following a literature procedure [26,27], two equivalents of N-bromosuccinimide in chloroform are engaged, but in our hands, the dibrominated compound 8 cannot be isolated, observing the decomposition of the starting material 7. Changing the experimental conditions such as the temperature or solvents is not successful. Therefore, in order to prepare the dibrominated compounds 8a-f, a second strategy is carried out starting from the previously described dinitro terthiophene derivative 5. In the first step, 5 was dibrominated in DMF with two equivalents of N-bromosuccinimide leading to compound 9 in good yield (66 %). In this reaction, choosing a high polar solvent (DMF) proves to be important because of the low reactivity of the terthiophene derivative 8. The two nitro groups are then reduced using iron in acetic acid at 60 °C, to obtain quantitatively (95 %) and selectively the diamino compound 10. reaction (from 10 to 50 %), pushed us to adopt a third strategy where the solubility is carried out upstream from the synthetic route. In this approach, 9 is engaged in a double Stille crosscoupling reaction with the monostannylated donor 11, leading to the extended dinitro π-system 13. The two nitro groups are then quantitatively reduced (95 %) using iron in acetic acid and condensation of the diamino corresponding compound 14 with 9,10-phenanthrenedione allowed the formation of 12f (28 % yield) which could not be isolated using the precedent synthetic strategy. On the whole, the second and the third synthetic strategies here mentioned consist in four steps (from compounds 5 to 12). The third synthetic route feasibility remains only experimented to obtain compounds 12b and 12f. For 12b, the two synthetic routes are quite identical concerning the elaboration overall yield (Table 4). For 12f, the only efficient preparative pathway, for solubility reasons, is the third strategy, that gives an overall yield of 5.3 % over the four steps.

Condensation with

Electronic and optical properties of compounds 12

The UV-Vis maximum absorption of these thienopyrazine-based compounds highlights a significant increase due to the extended conjugation (Fig. 7a). Concerning 12f extra conjugation is reached because of the rigidification of the π-conjugated system in a flat conformation thanks to the phenanthrene substructure. The experimental UV-Vis spectrum of 12f gives an absorption maximum of 742 nm. It reveals that 12f absorbs in the whole domain of the visible light (from 400 nm to 900 nm), exhibiting their interest to develop organic solar cells (Table 5). The HOMO-LUMO orbital diagrams shown in Fig. 8 demonstrate that the HOMO orbital is delocalized on the five central thiophene rings, while the LUMO orbital is concentrated on the thienopyrazine subunit. As for 7c and 7d, absorption properties of 12c and 12d are not influenced by the para substituent (methoxy or fluorine) on the phenylene ring.

To experimentally determine HOMO and LUMO energy levels of compounds 12, cyclic voltammetry analysis is additionally performed in 0.1 M of TBAPF6 solution in dichloromethane using a 50 mV/s scan speed.

Experimental and calculated frontiers orbitals levels values are recorded in Table 5 and are in good correlation, indicating the relevance of the DFT method to predict and value the electronic properties of this kind of compounds.In the solid state obtained from drop casted films from CHCl3 solutions, 30-50 nm red-shifts are recorded for 12b-d in the absorption spectra compared to the solution ones (Fig. 7b). Interestingly, for 12f, the bathochromic shift is only 15 nm, probably revealing, its more planar conformation in the solution state compared to the other compounds.

Conclusion

The strategy here presented to elaborate thienopyrazine based donor acceptor lowband-gap compounds is based on the understanding of the electronic contribution of the building block thienopyrazine. The first step is the synthesis and the characterization of the smallest thienopyrazine models to validate the required electronic properties and the good accordance between experimental data and theoretical calculations. In the second step, the π-conjugated system is elongated and the same methodology is implemented leading to conclude that the thienopyrazine substructure is suitable to build fully extended π-conjugated targets. After these insightful results, we take advantage of thienopyrazine based compounds to lower the LUMO level and to combine it with donor compounds having a high HOMO level in order to elaborate low band gaps systems.

In this way, four soluble extended conjugated targets are elaborated exhibiting absorption properties ranging from the entire visible spectrum to the near infrared region.

Experimental section

Chemicals and instrumentation

Unless otherwise noted, all chemicals were purchased from Aldrich, Alfa-Aesar, Fluorochem, TCI or Acros and used without further purification. TLC were performed using aluminium sheet covered by SiO2 Silica gel 60F254 (Merck), and column chromatography using Silica gel 60 (particle size 0.063-0.200 mm, Merck).

Microwaves reactions were performed in sealed tubes using a CEM Discover apparatus.

1H-NMR (400 MHz) and 13C-NMR (101 MHz) were recorded on Bruker Avance 400. Chemical shifts are given in ppm and coupling constants J in Hz. The residual signal of the solvent was taken as internal reference standard.

IR spectra were recorded using a Perkin-Elmer Spectrum 100 ATR spectrophotometer. Absorption spectra were recorded using a Hewlett-Packard 8453 spectrophotometer.

Fluorecence spectra were recorded with a Perkin Elmer LS55. Cyclic Voltammetry was performed in a three electrodes cell filled with a CH3CN solution of Bu4NPF6 (0.1 M) using EGG 273 potentiostat. Platinum wire electrodes were used as working and counter electrodes. A silver wire was used as pseudo-reference electrode (calculated using a 1.5 mM solution of ferrocene/ferrocinium couple).

EI-HRMS measurements were made on a Waters AutoSpec 6 and MALDI-ToF experiments on a Waters QToF Premier.

Infrared absorption spectra were recorded on a Perkin Elmer 1000 spectrometer. The photoelectron spectra were recorded on the homemade "PhenixI" instrument equipped with 127° cylindrical analyser and monitored by a micro-computer supplemented with a digital analogic converter. The spectra were calibrated with the known auto-ionisation of helium at 4.99 eV [He(II)/He(I)] and xenon ionisations at 12.13 and 13.44 eV. All the products were vaporised thanks to the beam heat at 10-6 mbar. We maintain a stable temperature of the beam in order to vaporize enough compound and obtain intense spectra. Compounds 5, 6, 9 and 10 were prepared following literatures protocols [23,24].

Computational details

Calculations were performed with the Gaussian 09 suite of programs [START_REF] Frisch | Gaussian 09, Revision D.01[END_REF] using the density functional method [START_REF] Parr | Functional Theory of Atoms and Molecules[END_REF]. The hybrid exchange functional B3LYP [START_REF] Becke | [END_REF][31][32] set were used. All atoms have been described with a 6-31G(d,p) basis set. Geometry optimisations were carried out without any symmetry restrictions, the minimum was verified with analytical frequency calculations. The UV/Visible absorption spectra were calculated within the Timedependent DFT formalism available in the Gaussian code using B3LYP/6-31G(d,p) level of theory.

Experimental procedure and characterization data

General procedures for the preparation of thienopyrazines 3a-d Method 1: In a 50 mL two-necked round-bottom flask were added under nitrogen 3.21 mmol (1.2 eq.) of thiophene-3,4-diammonium chloride 1 and 2.67 mmol (1.0 eq.) of the 1,2 diketone 2 in 8.0 mL of absolute ethanol. Then, 0.9 mL of triethylamine was added dropwise and the mixture was stirred and refluxed for 3 days. After evaporation of the solvent under reduced pressure, the residue is purified by flash chromatography.

Method 2:

In an adequate microwaves vessel were added under nitrogen 1.07 mmol (1.2 eq.) of thiophene-3,4-diammonium chloride and 0.890 mmol (1.0 eq.) of the 1,2-diketone 2 in 7.0 mL of absolute ethanol. 0.7 mL of triethylamine was then added and the reaction mixture was stirred and warmed up at 150 °C for 10 min under microwave radiations (200 W). After evaporation of the solvent under reduced pressure, the residue is purified by flash chromatography.

2,3-Diethylthieno[3,4-b]pyrazine 3a (Z = Et)

The crude product, synthesized by method 1, is purified by flash chromatography using cyclohexane/ CH2Cl2 as the eluent (75/25-0/100) to obtain 3a (260 mg, 51 %) as a brownish powder.

NMR 1H (CHCl3), δ (ppm): 7.77 (s, 2H, HTh), 2.88 (q, J = 7.4 Hz, 4H, CH2 H ), 1.33 (t, J = 7.4 Hz, 6H, CH3 H ). NMR 13C (CHCl3), δ (ppm): 156.9, 141.

2,3-Diphenylthieno[3,4-b]pyrazine 3b (Z = Ph)

The crude product is purified by flash chromatography using CH2Cl2/EtOAc (100/0-75/25) to obtain 3b (method 1: 300 mg, 26 %, method 2: 60 mg, 20 %) as a brownish powder.

NMR 1H (CHCl3), δ (ppm):

2,3-Bis(4-methoxyphenyl)thieno[3,4-b]pyrazine 3c (Z = p-C6H4OMe)

The crude product, synthesized by method 1, is purified by flash chromatography using cyclohexane/ CH2Cl2 as the eluent (75/25-0/100) to obtain 3c (430 mg, 22 %) as a burgundy powder.

NMR

2,3-Bis(4-fluorophenyl)thieno[3,4-b]pyrazine 3d (Z = p-C6H4F)

The crude product is purified by flash chromatography using CH2Cl2 as the eluent to obtain 3d (method 1: 200 mg, 43 %, method 2: 80 mg, 30 %), as a pink powder.

NMR

General procedure for the preparation of thienopyrazines 7a-f Method 1:

In a 25 mL two-necked round-bottom flask were added under nitrogen 200 mg (0.718 mmol, 1.5 eq.) of diamine 6 and 0.478 mmol (1.0 eq.) of the 1,2-diketone 2 in 6.0 mL of absolute ethanol. The mixture was then stirred and refluxed for 4 days. After evaporation of the solvent under reduced pressure, the residue was purified by flash chromatography.

Method 2:

In an adequate microwaves vessel were added under nitrogen 246 mg (0.886 mmol, 1.0 eq.) of diamine 6 and 0.886 mmol (1.0 eq.) of the 1,2-diketone 2 in 4.0 mL of absolute ethanol. The reaction mixture was stirred and warmed up at 150 °C for 20 min under microwaves irradiation (200 W). After evaporation of the solvent under reduced pressure, the residue was purified by flash chromatography.

2,3-Diethyl-5,7-di(thiophen-2-yl)thieno[3,4-b]pyrazine 7a (Z = Et)

The crude product, synthesized by method 2, is purified by flash chromatography using cyclohexane/ CH2Cl2 as the eluent (75/25 to 50/50) to obtain 7a (200 mg, 63 %) as a red powder.

NMR 1H (CHCl3), δ (ppm): 7.61 (dd, J = 3.7 Hz, J = 1.1 Hz, 2H, HTh), 7.36 (dd, J = 5.1 Hz, J = 1.1 Hz, 2H, HTh), 7.10 (dd, J = 5.1 Hz, J = 3.7 Hz, 2H, HTh), 2.95 (q, J = 7.2 Hz, 4H, CH2 H ), 1.49 (t, J = 7.2 Hz, 6H, CH3 H ). NMR 13C (CHCl3), δ (ppm): 156.9, 137.9, 135.0, 127. UV (CHCl3) 10-4 M, λmax: 504 nm. MS (EI) m/z (%): 355.9 (100, M+), 57.0 (39), 71.0 (26). MSHR (EI): M+ Calculated 356.0470; Found 356.0471.

2,3-Diphenyl-5,7-di(thiophen-2-yl)thieno[3,4-b]pyrazine 7b (Z = Ph)

The crude product, synthesized by method 1, is purified by flash chromatography using cyclohexane/ CH2Cl2 as the eluent (50/50 to 0/100) to obtain 7b (210 mg, 65 %) as a pink powder.

NMR 1H (CHCl3), δ (ppm): 7.68 (dd, J = 3.7 Hz, J = 1.1 Hz, 2H, HTh), 7.62-7.58 (m, 4H, HTh et HPh), 7.41-7.34 (m, 8H, HPh et HPh), 7.12 (dd, J = 5.1 Hz, J = 3.7 Hz, 2H, HTh). NMR 13C (CHCl3), δ (ppm): 153.0, 147. [START_REF] Shinar | Organic Electronics in Sensors and Biotechnology[END_REF] UV λmax (CHCl3) 10-4 M, λmax: 550 nm. MS (EI) m/z (%): 451.9 (100, M+), 201.9 (15). MSHR (EI): M+ Calculated 452.0470; Found 452.0473.

2,3-Bis(4-methoxyphenyl)-5,7-di(thiophen-2-yl)thieno[3,4-b]pyrazine 7c (Z = p-C6H4OMe)

The crude product, synthesized by method 1, is purified by flash chromatography using CH2Cl2/EtOAc as the eluent (100/0-25/75) to obtain 7c (100 mg, 41 %) as a burgundy powder.

NMR 1H (CHCl3), δ (ppm): 7.64 (dd, J = 3.7 Hz, J = 1.1 Hz, 2H, HTh), 7.58 (d, J = 8.9 Hz, 4H, HPh), 7.38 (dd, J = 5.1 Hz, J = 1.1 Hz, 2H, HTh), 7.11 (dd, J = 5.1 Hz, J = 3.7 Hz, 2H, HTh), 6.88 (d, J = 8.9 Hz, 4H, HPh), 3.85 (s, 6H, HOMe). NMR 13C (CHCl3), δ (ppm): 160.

2,3-Di(pyridin-2-yl)-5,7-di(thiophen-2-yl)thieno[3,4-b]pyrazine 7e (Z = 2-pyridyl)

The crude product, synthesized by method 2, is purified by flash chromatography using CH2Cl2/EtOAc as the eluent (75/25) to obtain 7e (100 mg, 25 %) as a dark pink powder.

10,12-Di(thiophen-2-yl)dibenzo[f,h]thieno[3,4-b]quinoxaline 7f (Z = 2,2′-diphenyl)

The crude product, synthesized by method 2, is purified by flash chromatography using cyclohexane/ CH2Cl2 as the eluent (50/50 to 0/100) to obtain 7f (210 mg, 53 %) as a green powder.

NMR 1H (250 MHz, CHCl3), δ (ppm): In a 250 mL two-necked round-bottom flask were added under nitrogen 1.67 g (3.83 mmol, 1.0 eq.) of diamine 9 and 804 mg (3.83 mmol, 1.0 eq.) of diphenylethanedione (benzil) 2b in 95 mL of glacial acetic acid. The mixture was then stirred and warmed up at 60 °C for 26 h. After cooling the reaction mixture to room temperature, the pink precipitate was filtered off on büchner, washed with cold absolute ethanol and dried under vacuum. Thienopyrazine 8b was obtained as a pink powder (1.23 g, 53 %).

NMR 1H (CDCl3), δ (ppm): 7.57-7.54 (m, 3H, HPh),

5,7-Bis(5-bromothiophen-2-yl)-2,3-bis(4-methoxyphenyl)thieno[3,4-b]pyrazine 8c (Z = p-C6H4OMe)

In a 250 mL two-necked round-bottom flask were added under nitrogen 1.67 g (3.83 mmol, 1.0 eq.) of diamine 9 and 1.03 g (3.83 mmol, 1.0 eq.) of 1,2-bis(4methoxyphenyl)ethane-1,2-dione 2c in 95 mL of glacial acetic acid. The mixture was then stirred and warmed up at 60 °C for 36 h. After cooling the reaction mixture to room temperature, the burgundy precipitate was filtered off on büchner, washed with cold absolute ethanol and dried under vacuum. Thienopyrazine 8c was obtained as a burgundy powder (1.21 g, 47 %).

NMR 1H (CDCl3), δ (ppm): 7.54 (d, J = 8.9 Hz, 4H, HPh), 7.25 (d, J = 4.0 Hz, 2H, HTh), 7.03 (d, J = 4.0 Hz, 2H, HTh), 6.89 (d, J = 8.9 Hz, 4H, HPh), 3.86 (s, 6H, OCH3). NMR 13C (CDCl3), δ (ppm): 160.

5,7-Bis(5-bromothiophen-2-yl)-2,3-bis(4-fluorophenyl)thieno[3,4-b]pyrazine 8d (Z = p-C6H4F)

In a 100 mL two-necked round-bottom flask were added under nitrogen 439 mg (1.01 mmol, 1.0 eq.) of diamine 9 and 247 mg (1.01 mmol, 1.0 eq.) of 1,2-bis(4-fluorophenyl)ethane-1,2-dione 2d in 25 mL of glacial acetic acid. The mixture was then stirred and warmed up at 60 °C for 36 h. After cooling the reaction mixture to room temperature, the pink precipitate was filtered off on büchner, washed with cold absolute ethanol and dried under vacuum. In a 250 mL two-necked round-bottom flask were added under nitrogen 1.67 g (3.83 mmol, 1.0 eq.) of diamine 9 and 797 mg (3.83 mmol, 1.0 eq.) of 9,10-phenanthrenedione 2f in 95 mL of glacial acetic acid. The mixture was then stirred and warmed up at 60 °C for 36 h. After cooling the reaction mixture to room temperature, the green precipitate was filtered off on Büchner, washed with cold absolute ethanol and dried under vacuum.

Thienopyrazine 8f was obtained as a green powder (1.27 g, 55 %). Due to the poor solubility of the product in common organic solvents, no NMR spectra could be obtained. In a 50 mL two-necked round-bottom flask were added under nitrogen 200 mg (0.327 mmol, 1.0 eq.) of 8b, 534 mg (0.679 mmol, 2.1 eq.) of the monostannylated derivative 11 and 15 mg (0.013 mmol, 0.04 eq.) of Pd0(PPh3)4 in 3.0 mL of dry and degassed solvents (THF/DMF 1/1). The mixture was then stirred and warmed up at 80 °C for 48 h. After cooling the reaction mixture to room temperature, a saturated NH4F solution was added. The solution was extracted with 3 × 50 mL of diethyl ether; the organic phases were assembled, washed with brine and dried over MgSO4. The solvent was evaporated under reduced pressure and the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (100/0-50/50) as the eluent. 12b was obtained as a green powder (240 mg, 51 %). In a 50 mL two-necked round-bottom flask were added under nitrogen 200 mg (0.298 mmol, 1.0 eq.) of 8c, 517 mg (0.656 mmol, 2.2 eq.) of the monostannylated derivative 11 and 15 mg (0.013 mmol, 0.04 eq.) of Pd0(PPh3)4 in 3.0 mL of dry and degassed solvent (THF/DMF 1/1). The mixture was then stirred and warmed up at 65 °C for 48 h. After cooling the reaction mixture to room temperature, a saturated NH4F solution was added (20 mL). The solution was extracted with 3 × 50 mL diethyl ether, the organic phases were assembled, washed with brine and dried over MgSO4. The solvent was evaporated under reduced pressure and the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (100/0-50/50) as the eluent. 12c was obtained as a red powder (35 mg, 10 %).

5,7-Bis(5′-(2,5-bis((2-ethylheptyl)oxy)-4-(thiophen-2-yl)phenyl)-[2,2′-bithiophen]-5-yl)-2,3-bis(4-fluorophenyl)thieno[3,4-b]pyrazine (DAD system 12d)

In a 50 mL two-necked round-bottom flask were added under nitrogen 200 mg (0.327 mmol, 1.0 eq.) of 8d, 534 mg (0.679 mmol, 2.2 eq.) of the monostannylated derivative 11 and 15 mg (0.013 mmol, 0.04 eq.) of Pd0(PPh3)4 in 2.0 mL of dry and degassed solvent (THF/DMF 1/1). The mixture was then stirred and warmed up at 65 °C for 48 h. After cooling the reaction mixture to room temperature, a saturated NH4F solution was added (20 mL). The solution was extracted with 3 × 50 mL diethyl ether, the organic phases were assembled, washed with brine and dried over MgSO4. The solvent was evaporated under reduced pressure and the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (100/0-50/50) as the eluent. 12d was obtained as a green powder (45 mg, 10 %).

NMR 1H {19F} (C6D6), δ (ppm): 7.61 (dd, J = 3.6, J = 1.1 Hz, 2H, H1), 7.55-7.53 (m, 4H, H9), 7.41-7.37 (m, 8H, H4 and H5,6,7,8), 7.27 (d, J = 3.9 Hz, 2H, H5,6,7,8), 7.23 (d, J = 3.9 Hz, 2H, H5,6,7,8), 7.08 (dd, J = 5.1, J = 1.1 Hz, 2H, H3), 6.97 (dd, J = 5.1, J = 3.6 Hz, 2H, H2), 6.80-6.75 (m, 4H, H10), 3.86 (t, J = 5.2 Hz, 8H, O-CH2),

5,5″″-Bis(2,5-bis((2-ethylhexyl)oxy)-4-(thiophen-2-yl)phenyl)-3″,4″-dinitro-2,2′:5′,2″:5″,2′″:5′″,2″″-quinquethiophene 13

In a 100 mL two-necked round-bottom flask were added under nitrogen 1.37 g (2.77 mmol, 1.0 eq.) of dibrominated compound 9, 4.36 g (5.54 mmol, 2.0 eq.) of the monostannylated derivative 11 and 15 mg (0.111 mmol, 0.04 eq.) of Pd0(PPh3)4 in 20 mL of dry and degassed solvent (DMF/THF 4/1). The mixture was then stirred and warmed up at 80 °C for 16 h. After cooling the reaction mixture to room temperature, the mixture was filtered on celite, washed with 100 mL of CH2Cl2 and the filtrate extracted with 2 × 100 mL of CH2Cl2. All the organic phases were assembled, washed with brine (3 × 100 mL) and dried over MgSO4. After evaporation of the solvent under reduced pressure, the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (100/0-70/30) as the eluent. Compound 13 was obtained as a red powder (2.15 g, 30 %).

NMR 1H (C6D6), δ (ppm): 7.61 (dd, J = 3.7 Hz, J = 1.2 Hz, 2H, HTh), 7.49 (d, J = 3.7 Hz, 2H, HTh), 7.39 (s, 4H, HPh), 7.12-7. In a 250 mL two-necked round-bottom flask were added under nitrogen 2.15 g (1.60 mmol, 1.0 eq.) of the dinitro compound 13 and 2.30 g (41.0 mmol, 25 eq.) of iron powder in 60 mL of glacial acetic acid. The mixture was then stirred and warmed up at 60 °C for 16 h. After cooling the reaction mixture to room temperature, the solvent was evaporated under reduced pressure and the residue is filtered on a plug of silica gel using CH2Cl2 as the solvent. After evaporation of the solvent under reduced pressure, 1.6 g of compound 14 was obtained (78 %) and was directly used in the next step because of its low stability towards air. In a 100 mL two-necked round-bottom flask were added under nitrogen 636 mg (0.50 mmol, 1.0 eq.) of diamine 14 and 115 mg (0.55 mmol, 1.1 eq.) of diphenylethanedione (benzyl) (2b) in 25 mL of glacial acetic acid and 10 mL of CHCl3. The mixture was then stirred and refluxed for 4 h. After cooling the reaction mixture to room temperature, the solvent was evaporated under reduced pressure and the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (80/20-60/40) as the eluent. Compound 12b was obtained as a pink powder (374 mg, 52 %). In a 250 mL two-necked round-bottom flask were added under nitrogen 1.60 g (1.26 mmol, 1.0 eq.) of diamine 14 and 275 mg (1.32 mmol, 1.05 eq.) of 9,10-phenanthrenedione (2f) in 25 mL of glacial acetic acid and 10 mL of CHCl3. The mixture was then stirred and refluxed for 4 h. After cooling the reaction mixture to room temperature, the solvent was evaporated under reduced pressure and the residue was purified on silica by flash chromatography using cyclohexane/ CH2Cl2 (80/20-60/40) as eluent. Compound 12f was obtained as a dark pink powder (510 mg, 28 %).

Fig. 1 :

 1 Fig. 1: Synthesis of thienopyrazine series 3a-f.

Fig. 2 :

 2 Fig. 2: HOMO and LUMO molecular orbitals correlation diagram of 7f from molecular orbital fragments.

Fig. 3 :

 3 Fig. 3: Gas phase ultraviolet photoelectron spectrum (UPS) of 3b (a) and 7b (b).

Fig. 4 :

 4 Fig. 4: Synthesis of compounds 7a-f.

Fig. 5 :

 5 Fig. 5: Molecular orbitals HOMO and LUMO of 7c and 7f.

  the appropriate 1,2-disubstituted diketone 2 enables to obtain with the [(5-(2,5-bis((2-ethylhexyl)oxy)-4-(thiophen-2-yl)phenyl)thiophen-2-yl)tributylstannane] 11 leads to the isolated desired DAD π-conjugated segments 12a-d. Unfortunately, 12f containing the thienopyrazine fused phenanthrene subunit cannot be isolated because of the very low solubility of the dibrominated intermediate 8f. Likewise, the low yields in this last Stille coupling

Fig. 6 :

 6 Fig. 6: Two strategies for the synthesis of thienopyrazine based DAD systems.

Fig. 7 :

 7 Fig. 7: (a) Absorption spectrum of 12b-d and 12f in CHCl3. (b) Absorption spectrum of 12bd and 12f drop casted thin films from a CHCl3 solution.

Fig. 8 :

 8 Fig. 8: HOMO and LUMO molecular orbitals representation of 12f.

2 2 , 3 -

 23 Bis(4-fluorophenyl)-5,7-di(thiophen-2-yl)thieno[3,4-b]pyrazine 7d (Z = p-C6H4F) The desired product, synthesized by method 2, appeared as a pink solid in the reaction vessel, were filtered and washed with cold absolute ethanol and no extra flash chromatography purification was necessary. 7d was obtained (230 mg, 53 %) as a pink powder. NMR 1H {19F} (CHCl3), δ (ppm): 7.66 (dd, J = 3.7 Hz, J = 1.1 Hz, 2H, HTh), 7.60-7.55 (m, 4H, HPh), 7.40 (dd, J = 5.1 Hz, J = 1.1 Hz, 2H, HTh), 7.13 (dd, J = 5.1 Hz, J = 3.7 Hz, 2H, HTh), 7.09-7.03 (m, 4H, HPh). NMR 13C (CHCl3), δ (ppm): 163.5 (d, 1JC-F = 250.0 Hz), 151.6, 137.3, 135.0 (d, 4JC-F = 3.3 Hz), 134.5, 132.0 (d, 3JC-F = 8.4 Hz, 4C), 127.4, 126.9, 125.1, 124.7, 115.46 (d, 2JC-F = 21.8 Hz, 4C). NMR 19F {1H} (CHCl3), δ (ppm): 111.44. IR (cm-1): 3100, 3069, 1900, 1642, 1601, 1507, 1404, 1353, 1273, 1231, 1154, 1096, 1077, 1056, 1011, 986, 959, 831. UV (CHCl3) 10-4 M, λmax: 550 nm. MS (EI) m/z (%): 487.8 (100, M+), 201.9 (24). MSHR (EI): M+ Calculated 488.0282; Found 488.0281.

  NMR 1H (CHCl3), δ (ppm): 8.29-8.23 (m, 4H, HPy), 7.95-7.87 (m, 2H, HPy), 7.70 (dd, J = 3.7 Hz, J = 1.1 Hz, 2H, HTh), 7.41 (dd, J = 5.1 Hz, J = 1.1 Hz, 2H, HTh), 7.25-7.21 (m, 2H, HPy), 7.13 (dd, J = 5.1 Hz, J = 3.7 Hz, 2H, HTh

  , J = 4.0 Hz, 2H, HTh), 7.04 (d, J = 4.0 Hz, 2H, HTh). NMR 13C (CDCl3), δ (ppm): 153.

Thienopyrazine

  8d was obtained as a pink powder (580 mg, 89 %). NMR 1H {19F} (CDCl3), δ (ppm): 7.56-7.52 (m, 4H, HPh), 7.29 (d, J = 4.0 Hz, 2H, HTh), 7.10-7.04 (m, 6H, HTh et HPh). NMR 13C (CDCl3), δ (ppm): 163.6 (d, 1JF-C = 250.5 Hz), 152.1, 137.4, 135.8, 134.7 (d, 4JF-C = 3.5 Hz), 132.0 (d, 3JFC = 8.4 Hz), 130.0, 124.4, 124.2, 115.6 (d, 2JF-C = 21.8 Hz), 115.0. NMR 19F {1H} (CDCl3), δ (ppm): 111.05. IR (cm-1): 3233, 2162, 1654, 1600, 1507, 1429, 1403, 1352, 1315, 1299, 1266, 1248, 1228, 1155, 1096, 1076, 1064, 1012, 983, 968, 832, 815, 798, 777, 751, 728, 687. MS (APCI) m/z (%): 644 (40, M+), 413 (20). MSHR (APCI): M+ Calculated 644.8570; Found 644.8599.10,12-Bis(5-bromothiophen-2-yl)dibenzo[f,h]thieno[3,4-b]quinoxaline 8f (Z = 2,2′diphenyl)

  To a solution of 2,2′-(2,5-bis((2-ethylhexyl)oxy)-1,4-phenylene)dithiophene (2.23 mmol, 1.11 g) in 20 mL of anhydrous THF was added dropwise at -60 °C under nitrogen n-butyllithium (2.23 mmol, 1.4 mL, 1.6 M in hexanes). The mixture was slowly warmed to 0 °C. After cooling to -10 °C, tributyltin chloride (2.29 mmol, 0.62 mL), was added in one portion and the solution was allowed to warm to room temperature overnight. Subsequently, the solvent was evaporated under reduced pressure and the medium was dissolved in dichloromethane, filtrated on celite, and then concentrated to give 11 as a brown oil. This product was used in the next step without further purification. NMR 1H (CDCl3), δ (ppm): 7.65 (d, J = 3.2 Hz, 1H, HTh), 7.53 (dd, J = 4.0 Hz, J = 1.2 Hz, 1H, HTh), 7.33 (dd, J = 5.2 Hz, J = 1.2 Hz, 1H, HTh), 7.26 (m, 2H, HPh), 7.16 (d, J = 3.6 Hz, 1H, HTh), 7.09 (dd, J = 4.8 Hz, J = 3.6 Hz, 1H, HTh), 3.98 (d, J = 5.6 Hz, 4H, O-CH2), 1.85 (quint, 2H, CH), 1.67-1.28 (m, 28H, CH2), 1.13 (m, 6H, CH2), 0.97-0.88 (m, 21H, CH3). 5,7-Bis(5′-(2,5-bis((2-ethylheptyl)oxy)-4-(thiophen-2-yl)phenyl)-[2,2′-bithiophen]-5-yl)-2,3-diphenylthieno[3,4-b]pyrazine (DAD system 1b) -Second synthetic strategy

  NMR 1H (C6D6), δ (ppm): 7.62-7.60 (m, 8H, H1 et H9), 7.50 (d, J = 3.8 Hz, 2H, H5,6,7,8), 7.39 (s, 4H, H4), 7.24 (d, J = 3.8 Hz, 2H, H5,6,7,8), 7.22 (d, J = 3.8 Hz, 2H, H5,6,7,8), 7.14-7.11 (m, 6H, H10,11), 7.08 (dd, J = 5.2, J = 1.2 Hz, 2H, H3), 6.97 (dd, J = 5.2, J = 3.6 Hz, 2H, H2), 3.86 (t, J = 5.0 Hz, 8H, O-CH2), 1.70-1.53 (m, 8H, CH2-CH3), 1.36-1.25 (m, 24H, CH2 alkyl), 0.99-0.90 (m, 24H, CH3). NMR 13C (151 MHz, CDCl3), δ (ppm): 152.5, 149.3, 149.3, 139.3, 139.0, 138.8, 137.9, 137.0, 136.9, 133.6, 130.1, 128.9, 128.0, 126.7, 126.1, 125.8, 125.6, 125.3, 124.8, 124.7, 123.8, 123.7, 123.2, 122.4, 112.5, 111.8, 71.7, 71.6, 39.9, 39.9, 31.0, 30.9, 29.5, 29.4, 24.4, 23.5, 23.5, 14.4, 14.4, 11.5, 11.5. IR (cm-1): 3062, 2954, 2920, 2854, 2160, 2027, 1737, 1603, 1532, 1479, 1462, 1437, 1400, 1378, 1355, 1263, 1209, 1069, 1027, 985, 927, 844, 778, 768, 698. UV (CHCl3) 10-4 M, λmax: 648 nm. MS (MALDI) m/z (%): 1445.5 (100, M+), 1030 (8), 949 (7).

  NMR 1H (C6D6), δ (ppm): 7.67 (d, J = 8.9 Hz, 4H, H9), 7.63-7.61 (m, 4H, H1), 7.51 (d, J = 3.9 Hz, 2H, H5,6,7,8), 7.40 (s, 4H, H4), 7.29 (d, J = 3.9 Hz, 2H, H5,6,7,8), 7.26 (d, J = 3.9 Hz, 2H, H5,6,7,8), 7.07 (dd, J = 5.1, 1.2 Hz, 2H, H3), 6.97 (dd, J = 5.1, 3.6 Hz, 2H, H2), 6.75 (d, J = 8.9 Hz, 4H, H10), 3.86 (t, J = 4.6 Hz, 8H, O-CH2), 3.28 (s, 6H, OMe), 1.68-1.51 (m, 8H, CH2-CH3), 0.99-0.90 (m, 24H, CH2 alkyl), 1.32-1.29 (m, 24H, CH3). NMR 13C (151 MHz, C6D6), δ (ppm): 161.3, 153.1, 150.6, 150.5, 140.4, 139.5, 139.3, 139.1, 138.3, 134.9, 132.8, 132.4, 128.7, 127.4, 127.2, 126.3, 126.3, 125.0, 124.6, 124.3, 123.7, 114.3, 113.9, 113.1, 72.5, 72.3, 55.2, 40.5, 31.6, 31.5, 30.0, 29.9, 24.9, 24.8, 23.9, 23.8, 14.7, 14.7, 11.8, 11.8.

  10 (d, J = 4 Hz, 2H, HTh), 7.08-7.04 (m, 4H, HTh), 6.98-6.94 (m, 4H, HTh), 3.87 (d, J = 5.4 Hz, 4H, O-CH2), 3.85 (d, J = 5.4 Hz, 4H, O-CH2), 1.82-1.68 (m, 4H, CH), 1.67-1.40 (m, 16H, CH2), 1.39-1.22 (m, 16H, CH2), 0.99-0.84 (m, 24H, CH3). NMR 13C (CDCl3), δ (ppm): 149.58 (CPh-O), 149.55 (CPh-O), 144.2 (CTh), 140.7 (CTh-NO2), 139.3 (CTh), 135.5(CTh), 135.2(CTh), 133.4(CTh), 132.2(CTh), 126.9 (CHTh), 126.2 (CHTh), 126.1 (CHTh), 126.0 (CHTh), 125.6 (CHTh), 125.3 (CHTh), 124.0 (CHTh), 123.9 (CPh), 121.9 (CPh), 112.7 (CHPh), 111.9 (CHPh), 72.2 (CH2-O), 72.0 (CH2-O), 39.9 (CH), 39.8 (CH), 31.0 (CH2), 30.8 (CH2), 29.4 (CH2), 29.3 (CH2), 24.3 (CH2), 24.2 (CH2), 23.3 (CH2), 23.2 (CH2), 14.28 (CH3), 14.25 (CH3), 11.5 (CH3), 11.4 (CH3). IR (cm-1): 2958, 2927, 2871, 1538, 1438, 1389, 1212, 1032, 787, 694. MS (MALDI) m/z (%): 1316.4 (M+, 100).

5, 7 -

 7 Bis(5′-(2,5-bis((2-ethylhexyl)oxy)-4-(thiophen-2-yl)phenyl)-[2,2′-bithiophen]-5-yl)-2,3diphenylthieno[3,4-b]pyrazine 12b -Third synthetic strategy

10, 12 -

 12 Bis(5′-(2,5-bis((2-ethylhexyl)oxy)-4-(thiophen-2-yl)phenyl)-[2,2′-bithiophen]-5yl)dibenzo[f,h] thieno[3,4-b]quinoxaline 12f

  NMR 1H (C6D6), δ (ppm): 9.03 (dd, J = 1.0 Hz, J = 7.7 Hz, 2H, H10), 7.84 (d, J = 8.8 Hz, 2H, H13), 7.64 (dd, J = 1.0 Hz, J = 2.5 Hz, 2H, H3), 7.51 (d, J = 3.8 Hz, 2H, H6), 7.40 (s, 2H, H4 or 5), 7.39 (s, 2H, H4 or 5), 7.36 (dd, J = 7.6 Hz, J = 7.7 Hz, 2H, H11), 7.28 (dd, J = 7.6 Hz, J = 8.8 Hz, 2H, H12), 7.22 (d, J = 3.6 Hz, 2H, H7), 7.19 (d, J = 3.6 Hz, 2H, H8), 7.10 (dd, J = 1.1, 5.1, 2H, H10), 7.00 (d, J = 3.6 Hz, 2H, H2), 6.98 (d, J = 3.6 Hz, 2H, H9), 3.96-3.87 (m, 8H, O-CH2), 1.91-1.78 (m, 4H, CH), 1.75-1.42 (m, 16H, CH2), 1.42-1.27 (m, 16H, CH2), 1.2-0.88 (m, 24H, CH3). NMR 13C (C6D6), δ (ppm): 150.4 (CPh-O), 150.3 (CPh-O), 142.7 (C=N), 140.4 (CTh), 139.2 (CTh), 138.80 (CTh), 138.78 (CTh), 138.5 (CTh), 134.8 (CTh), 132.9 (CTh), 131.4 (CAr), 130.4 (C-H12), 128.5 (CAr), 128.3 (C-H11), 127.5 (C-H10), 127.0 (C-H6), 126.2 (C-H3), 125.3 (C-H7), 124.1 (C-H2), 124.0 (C-H8), 123.8 (CPh), 123.6 (CPh), 123.2 (C-H13), 113.4 (CHPh), 112.5 (CHPh), 72.4 (CH2-O), 72.3 (CH2-O), 40.4 (2 CH), 31.6 (CH2), 31.4 (CH2), 30.1 (CH2), 30.0 (CH2), 24.83 (CH2), 24.78 (CH2), 24.0 (CH2), 23.9 (CH2), 14.9 (CH3), 14.8 (CH3), 11.94 (CH3), 11.88 (CH3). IR (cm-1): 3067, 2956, 2925, 2858, 1603, 1533, 1490, 1466, 1436, 1402, 1380, 1353, 1295, 1211, 1109, 1033, 846, 783, 760, 722, 693. MS (MALDI) m/z (%): 1442.5 (M+, 100). UV (CHCl3) 10-4 M, λmax: 739 nm.

Table 1 :

 1 Electronic properties of compounds 3a-d.

Table 2 :

 2 Electronic properties of compounds 7.

Table 3 :

 3 Calculated HOMO and LUMO levels and electronic band-gap Egap (eV) of compounds 3 and 7.

Table 4 :

 4 Yields of compounds 12.

Table 5 :

 5 Optical and electronic properties of compounds 12 in solution.

  6, 152.6, 134.9, 131.82, 131.65, 127.4, 126.6, 124.5, 113.7, 105.7, 94.7, 89.7, 55.5. IR (cm-1): 3073, 2995, 2958, 2927, 2835, 2049, 1600, 1573, 1509, 1456, 1440, 1410, 1354, 1289, 1245, 1170, 1078, 1028, 982, 923, 824. UV λmax (CHCl3) 10-4 M, λmax: 546 nm. MS (EI) m/z (%): 511.9 (100, M+). MSHR (EI): M+ Calculated 512.0683; Found 512.0683.

  7.51-7.36 (m, 10H, HTh et HPh), 7.25-7.21 (m, 2H, HTh), 7.14-7.11 (m, 2H, HTh). NMR 13C (CDCl3), δ (ppm): 153.5, 138.8, 137.7, 136.1,

	130.14, 130.01, 129.4, 128.3, 124.37, 124.23, 114.9.
	IR (cm-1): 3101, 3063, 2968, 2930, 2897, 2734, 2578, 2161, 1790, 1705, 1633, 1572,
	1537, 1497, 1454, 1443, 1427, 1414, 1373, 1337, 1262, 1239, 1225, 1200, 1159, 1127, 1053,
	1022, 972, 929, 908, 847, 830, 812, 798, 781, 741.
	UV (CHCl3) 10-4 M, λmax: 630 nm.
	MS (EI) m/z (%): 449.8 (100, M+). MSHR (EI): M+ Calculated 450.0314; Found
	450.0313.

  5, 138.8, 137.7, 136.1, 130.14, 130.01, 129.4, 128.3, 124.37, 124.23, 114.9. IR (cm-1): 3050, 1709, 1501, 1426, 1349, 1319, 1271, 1246, 1233, 1208, 1179, 1088, 1062, 1025, 999, 984, 970, 899, 854, 809, 781, 771, 766, 729, 716, 699, 689, 665.

MS (EI) m/z (%): 609

(14)

, 430

(27)

, 413

[START_REF] Becke | [END_REF]

, 178 (75), 161 (70), 143 (100). MSHR (EI): M+ Calculated 607.8680; Found 607.8688.

  Hz), 132.0 (d, 3JC-F = 8.2 Hz), 130.5, 126.7, 126.1, 125.8, 125.6, 125.4, 123.8, 123.6, 123.2, 122.3, 115.2 (d, 2JC-F = 21.6 Hz), 115.2, 114.8, 112.5, 111.8, 71.8, 71.6, 39.9, 39.9, 31.0, 30.9,29.5, 29.4, 24.4, 24.4, 23.5, 23.5, 14.4, 14.4, 11.5, 11.5.

	1.68-1.49 (m, 8H, CH2-CH3),
	1.39-1.26 (m, 24H, CH2 alkyl), 0.98-0.91 (m, 24H, CH3). NMR 13C (151 MHz, CDCl3), δ
	(ppm): 163.28 (d, 1JC-F = 251.0 Hz), 151.1, 149.3 (2C), 139.3, 139.1, 139.0, 137.7, 136.9,
	134.9 (d, 4JC-F = 3.1 NMR 19F {1H}
	(C6D6), δ (ppm): 111.44.
	IR (cm-1): 3066, 2955, 2922, 2856, 2161, 2039, 1711, 1601, 1532, 1508, 1486, 1459,
	1441, 1401, 1379, 1355, 1264, 1208, 1157, 1096, 1073, 1029, 986, 836, 816, 781, 728, 691.
	UV (CHCl3) 10-4 M, λmax: 660 nm.

MS (APCI) m/z (%): 1481 (100, M+). MSHR (APCI): M+ Calculated 1481.5299; Found 1481.5286.

Acknowledgement:

We gratefully acknowledge financial support of this work provided by the French Research Council, PHC Volubilis (n°24425WH), CNRS-CNRST (n°22579) and CAPES COFECUB (Ph 666/10) programs.


Isabelle Gounand
email: isabelle.gounand@eawag.ch

Eric Harvey
email: eric.harvey@eawag.ch

Chelsea J Little
email: chelseajean.little@eawag.ch

Florian Altermatt
email: florian.altermatt@eawag.ch

rooting the theory into the field

Keywords: resource flow, metaecosystem, metacommunity, subsidy, animal movement, landscape

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

Ecosystem couplings outside the metacommunity box

Spatial flows of energy, materials and organisms are ubiquitous in nature: Organisms move to forage for food, migrate or disperse, actively or as propagules [START_REF] Clobert | Dispersal[END_REF][START_REF] Bauer | Migratory animals couple biodiversity and ecosystem functioning worldwide[END_REF][START_REF] Reiners | Transport of energy, information and material through the biosphere[END_REF]. All these organismal movements along with passive flows of inorganic nutrients and detritus connect ecosystems and influence local ecosystem dynamics [START_REF] Holyoak | Metacommunities: spatial dynamics and ecological communities[END_REF][START_REF] Polis | Food Webs at the landscape level[END_REF]. Community ecologists have paid particular attention to spatial flows of species (dispersal; see Glossary), which connect several communities in a metacommunity [START_REF] Wilson | Complex interactions in metacommunities , with implications for biodiversity and higher levels of selection[END_REF][START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF]. The metacommunity framework demonstrated how dispersal and environmental heterogeneity determine species coexistence and biodiversity among a set of patches at local and regional scales [START_REF] Holyoak | Metacommunities: spatial dynamics and ecological communities[END_REF][START_REF] Carrara | Dendritic connectivity controls biodiversity patterns in experimental metacommunities[END_REF][START_REF] Cottenie | Integrating environmental and spatial processes in ecological community dynamics[END_REF][START_REF] Grainger | Dispersal and diversity in experimental metacommunities: linking theory and practice[END_REF][START_REF] Logue | Empirical approaches to metacommunities: a review and comparison with theory[END_REF][START_REF] Wang | Meta-ecosystems: a theoretical framework for a spatial ecosystem ecology[END_REF]. In parallel, the growing recognition that resources are not stationary and spatial flows of resources can also play a significant role in the dynamics of various types of biological communities [START_REF] Polis | Food Webs at the landscape level[END_REF] fostered the development of the metaecosystem framework [13,[START_REF] Massol | Linking community and ecosystem dynamics through spatial ecology[END_REF]. By explicitly integrating local production and spatial movement of resources within metacommunities, this framework demonstrated the feedback between community and resource dynamics across spatial scales [START_REF] Guichard | Recent advances in metacommunities and meta-ecosystem theories[END_REF]. Organisms moving among ecosystems modify the spatial distribution of resources, and thus habitat suitability, through local resource consumption and biomass recycling [START_REF] Gravel | Source and sink dynamics in meta-ecosystems[END_REF]. In parallel, resource flows connect the dynamics of distinct communities via the local production and export of these resources [START_REF] Gounand | Subsidies mediate interactions between communities across space[END_REF], and can trigger trophic cascades in recipient ecosystems [START_REF] Leroux | Subsidy hypothesis and strength of trophic cascades across ecosystems[END_REF]. This mechanistic link between community and ecosystem functioning and spatial dynamics makes the meta-ecosystem framework a powerful tool to investigate the dynamics of connected ecosystems. This is especially relevant in the context of increasing perturbations, where disruptions in local processes can spread in space through changes in spatial flows [START_REF] Gounand | Subsidies mediate interactions between communities across space[END_REF][START_REF] Fey | Recognizing cross-ecosystem responses to changing temperatures: soil warming impacts pelagic food webs[END_REF][START_REF] Krell | Aquatic prey subsidies to riparian spiders in a stream with different land use types[END_REF][START_REF] Harvey | Spatially cascading effect of perturbations in experimental metaecosystems[END_REF][START_REF] Harvey | Bridging ecology and conservation: From ecological networks to ecosystem function[END_REF]. However, while the theoretical development of the meta-ecosystem framework has been fast [13,[START_REF] Gravel | Source and sink dynamics in meta-ecosystems[END_REF][START_REF] Gounand | Stability and complexity in model meta-ecosystems[END_REF][24][START_REF] Gravel | Patch dynamics, persistence, and species coexistence in metaecosystems[END_REF][START_REF] Haegeman | General relationships between consumer dispersal, resource dispersal and metacommunity diversity[END_REF][START_REF] Marleau | Meta-ecosystem dynamics and functioning on finite spatial networks[END_REF][START_REF] Marleau | Nutrient flows between ecosystems can destabilize simple food chains[END_REF][START_REF] Marleau | Emergence of nutrient co-limitation through movement in stoichiometric meta-ecosystems[END_REF], adoption by empiricists has generally lagged behind [START_REF] Logue | Empirical approaches to metacommunities: a review and comparison with theory[END_REF], and mostly consisted of conceptual experiments paralleling the modelling work [START_REF] Gounand | Subsidies mediate interactions between communities across space[END_REF][START_REF] Harvey | Spatially cascading effect of perturbations in experimental metaecosystems[END_REF][START_REF] Limberger | Ecosystem flux and biotic modification as drivers of metaecosystem dynamics[END_REF] or a few applications to coastal systems [START_REF] Spiecker | Reciprocal feedbacks between spatial subsidies and reserve networks in coral reef meta-ecosystems[END_REF][START_REF] Menge | Are meta-ecosystems organized hierarchically? A model and test in rocky intertidal habitats[END_REF].

Here, we argue that empirical research on meta-ecosystems is progressing slowly due to the theory's high level of abstraction. As a conceptual extension of metacommunity theory, metaecosystem models usually tend to focus on dispersal to be the driver of organismal flows in space, thereby implicitly considering the couplings between ecosystem patches of similar habitats [START_REF] Gravel | Source and sink dynamics in meta-ecosystems[END_REF][START_REF] Gounand | Stability and complexity in model meta-ecosystems[END_REF]24,[START_REF] Haegeman | General relationships between consumer dispersal, resource dispersal and metacommunity diversity[END_REF]. Dispersal, however, is but a small subset of all organismal movement types that can couple ecosystems (Box 1). The missing piece -other types of organismal movementprevents current meta-ecosystem theory from providing predictions specific enough to apply to real ecosystems. Meanwhile, empirical ecology faces challenges in understanding how spatial flows induce mutual feedbacks among different ecosystems, partly due to technical challenges of measuring spatial flows [START_REF] Jacobson | Quantifying and disentangling dispersal in metacommunities : how close have we come ? How far is there to go ?[END_REF]. Empirical ecology is also traditionally divided into independent research domains which consider spatial flows only as external inputs to ecosystems of interest (e.g. or terrestrial vs. aquatic ecology [34]) and have different variables of interest and measurement units to describe the same processes (e.g. individuals or species dynamics in community ecology vs. biomass or nutrient cycles in ecosystem ecology). Given that ecosystems are commonly interconnected in complex networks of spatial flows [START_REF] Polis | Food Webs at the landscape level[END_REF][START_REF] Massol | Linking community and ecosystem dynamics through spatial ecology[END_REF], a more holistic spatial perspective, considering potential feedbacks among ecosystems, is critically needed to understand the dynamics of ecosystems and their responses to changes [START_REF] Bauer | Migratory animals couple biodiversity and ecosystem functioning worldwide[END_REF][START_REF] Harvey | Bridging ecology and conservation: From ecological networks to ecosystem function[END_REF][START_REF] Mitchell | Linking Landscape Connectivity and Ecosystem Service Provision: Current Knowledge and Research Gaps[END_REF][START_REF] Viana | Migratory Birds as Global Dispersal Vectors[END_REF][START_REF] Van Kleunen | Global exchange and accumulation of non-native plants[END_REF].

Incorporating more specific attributes of ecosystem spatial couplings in the metaecosystem framework would foster the production of applicable theoretical predictions and bridge previously less connected empirical fields. Here, we offer a new, synthetic vision of metaecosystems. We contend that (1) meta-ecosystems found in nature fall along a gradient of coupling types: some depend mostly on dispersal, and others mostly on spatial flows of resources; (2) these ecosystem coupling types occur at different spatial scales, with (3) different underlying drivers, including different types of organismal movement, which might affect metaecosystem dynamics in fundamentally different ways than dispersal alone (Box 1). We believe that integrating these organismal movements into meta-ecosystem models will raise the generality-realism trade-off to the appropriate level needed to further understand the mechanisms underlying spatial dynamics across natural landscapes (Box 2). Joining the ongoing effort to build an integrative and predictive ecology [START_REF] Mouquet | Predictive ecology in a changing world[END_REF][START_REF] Petchey | The ecological forecast horizon, and examples of its uses and determinants[END_REF], we propose a comprehensive framework for spatial ecology acknowledging how spatial flows of organisms and resources occur and interact at contrasting scales in nature. In the next sections we motivate this upgraded meta-ecosystem framework with concrete examples and identify the next theoretical and empirical steps needed for advancing spatial ecology.

Contrasting natures of ecosystem couplings: dispersal versus resource flows

Meta-ecosystem theory has extended the metacommunity framework with general models that include both dispersal and resource flows to connect ecosystems [13]. However, true dispersal, defined as the settlement and successful reproduction of individuals away from their place of birth [START_REF] Clobert | Dispersal[END_REF][START_REF] Ronce | How Does It Feel to Be Like a Rolling Stone? Ten Questions About Dispersal Evolution[END_REF][START_REF] Massol | How life-history traits affect ecosystem properties: effects of dispersal in meta-ecosystems[END_REF], can only occur between ecosystems offering similar enough physical habitats for the dispersing organism to survive in both. Clearly, many organisms have some adaptations to deal with variations in habitat conditions, and often can cope with what is considered -and built into models -as environmental heterogeneity. However, all organisms have some physiological limits preventing a successful dispersal across very different physical boundaries (e.g., freshwater to terrestrial or marine to freshwater). Generally, individuals crossing habitat barriers die and enter the detrital pool rather than reproduce and establish new populations in the recipient ecosystem (e.g. whales or algae grounding on beaches). Thus, dispersal primarily links similar ecosystems, while massive flows of resources are reported to cross ecosystem boundaries [START_REF] Polis | Food Webs at the landscape level[END_REF][START_REF] Polis | Toward an Integration of Landscape and Food Web Ecology: The Dynamics of Spatially Subsidized Food Webs[END_REF], therefore linking contrasting ecosystems. Though implicitly clear to most empiricists, theoreticians often do not explicitly make this distinction ( [START_REF] Leroux | Dynamics of Reciprocal Pulsed Subsidies in Local and Meta-Ecosystems[END_REF] for an exception), thereby ignoring implications for the nature of dominant ecosystem couplings (dispersal vs. resource flows). This has, in our eyes, led to the discrepancy between the theoretical advancements in meta-ecosystem theory and a lack of application to empirical systems.

One can imagine a gradient of meta-ecosystem couplings going from dispersal-based to resource-flow based meta-ecosystems (Figure 1A-B): at one end of the gradient, dispersal is more likely to occur between ecosystem patches of similar habitats, including similar abiotic and biotic characteristics, such as networks of ponds, islands, forest patches, or table mountains (Figure 1A). Organisms dispersing in such fragmented landscapes have to cross what is for them an unsuitable matrix of radically different habitats in order to reach the next acceptable patch. For instance, zooplankton transported by birds can only establish and survive in new aquatic habitats but are doomed to die when released during terrestrial stopovers [START_REF] Viana | Migratory Birds as Global Dispersal Vectors[END_REF]44]. Given that resources transported along with dispersal are probably negligible in comparison to the consumer effect of individuals founding new populations (see Box 1), these dispersal-based meta-ecosystems finally boil down to metacommunity-like dynamics coupled with local recycling (Figure 1A). At the opposite end of the gradient, massive cross-ecosystem flows of resources occur at the boundaries of contrasting ecosystems, such as at aquatic-terrestrial or pelagic-benthic interfaces (Figure 1B).

Resource flows can be mediated by physical processes passively transporting detritus or nutrients, such as wind (e.g. windblown leaves from forests accounting for up to 59% of litter nutrient input in adjacent orchards [45]), gravity (e.g. nutrient leaching in a watershed, zooplankton faecal pellets sinking from pelagic to benthic systems; [START_REF] Turner | Zooplankton fecal pellets, marine snow, phytodetritus and the ocean's biological pump[END_REF][START_REF] Didham | Agricultural intensification exacerbates spillover effects on soil biogeochemistry in adjacent forest remnants[END_REF]), or aquatic currents and tides (e.g., benthic nutrients re-suspended by upwelling currents, algae or carrion grounding on beaches; [48]). Thus, ecosystems do not have to be suitably similar in order to be connected by these types of resource flows, unlike those connected by dispersal.

Significant resource flows can also consist of organisms approaching the end of their life cycles, such as anadromous fish migrating from oceans to reproduce in streams or insects emerging from freshwater systems and dying on land [START_REF] Gratton | Flux of aquatic insect productivity to land: Comparison of lentic and lotic ecosystems[END_REF][START_REF] Gende | Magnitude and fate of salmon-derived nutrients and energy in a coastal stream ecosystem[END_REF][START_REF] Bultman | Terrestrial deposition of aquatic insects increases plant quality for insect herbivores and herbivore density[END_REF]. In Iceland, tundras surrounding lakes receive an additional input of nitrogen of up to 250 kg ha -1 yr -1 from aquatic midge fall, a level shown to cascade on plant nitrogen content and eventually increasing the density of herbivorous caterpillars [START_REF] Bultman | Terrestrial deposition of aquatic insects increases plant quality for insect herbivores and herbivore density[END_REF][START_REF] Gratton | Ecosystem Linkages Between Lakes and the Surrounding Terrestrial Landscape in Northeast Iceland[END_REF]. As a product of the animals' life-history cycles, these flows affect local community dynamics in a fundamentally different way than does dispersal, by primarily acting as resource inputs in the recipient ecosystem instead of contributing to population dynamics (Box 1). Finally, foraging movements of consumers searching for food in one habitat type but otherwise residing in another can also induce massive resource flows. As an example, in Kenya, Subaluski and colleagues estimated that a population of hippopotami grazing in savannahs transfers annually the production equivalent of 260 to 1563 hectares of terrestrial biomass through faeces into the Mara river, where the hippopotami rest and defecate [START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF]. Even if animal use the whole landscape, this asymmetry in animal activities induces strong resourcebased meta-ecosystem dynamics resulting in a huge net flow of resource between two contrasting adjacent ecosystems (other similar and emblematic examples include marine birds fishing in oceans but defecating guano ashore or bears hunting salmon in rivers and pulling their carcasses into forests [START_REF] Gende | Magnitude and fate of salmon-derived nutrients and energy in a coastal stream ecosystem[END_REF][START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF][START_REF] Adame | Nutrient subsidies delivered by seabirds to mangrove islands[END_REF][55]).

Overall, on our gradient of ecosystem couplings with increasing habitat dissimilarity, the two extremes represent widespread types of natural meta-ecosystems with opposing characteristics and dynamics: dispersal-based meta-ecosystems dominated by metacommunitylike dynamics occur between distant ecosystems but of similar habitat types at one end of the gradient (Figure 1A), while at the other end of the gradient, resource-flow based meta-ecosystems occur directly at the boundaries of dissimilar ecosystems (Figure 1B). Along this gradient, intermediate cases exist where dispersal and subsidies are both significant. For instance, uphill and downhill terrestrial habitats, or upstream and downstream sites of river networks are similar enough habitats to be coupled by dispersal of some organisms, while also being linked by passive flows of resources. While the existence of these different flow types structuring spatial dynamics within landscapes are generally acknowledged from field observations (Box 2), we still lack quantitative information about the frequency distribution (bimodal or more continuous) of natural meta-ecosystems along the gradient. Collecting and assembling such data is in our eyes one of the major empirical challenge to make further progress in spatial ecology (see last section for details).

Spatial scales of dispersal versus resource flows

Dispersal and resource-flow based meta-ecosystems do not necessarily operate in isolation from each other. Rather, the two types of flows and associated meta-ecosystem dynamics occur at different spatial scales within the landscape (Figure 1C). Resource-flow based meta-ecosystems occur at a local scale, across boundaries of adjacent ecosystems (e.g. litter fall or insect exchanges at lake-forest interface; [56]), while dispersal-based meta-ecosystems could connect these local meta-ecosystems at a larger scale via organisms crossing unsuitable matrices to disperse into distant but similar ecosystems (e.g. bird-dispersed plankton among lakes; [44]).

Thus, this conceptual framework encompasses spatially nested dynamics and integrates the contributions of landscape and meta-ecosystem ecology in a single framework for spatial ecology. On one hand, landscape ecology analyses the structure of heterogeneous landscapes, quantifying the surface and relative positions of the different elements (e.g. forest patches, lakes, fields) and their importance for focal species persistence. On the other hand, meta-ecosystem ecology concentrates on the dynamic aspect, by showing how spatial flows connect these structuring elements (Box 2). Acknowledging the different scales at which various spatial flows connect elements in the landscape is crucial both to understand species persistence and ecological processes in heterogeneous landscapes, and to anticipate how perturbations in one location propagate across the landscape [START_REF] Dreyer | Habitat linkages in conservation biological control: Lessons from the land-water interface[END_REF]. A local perturbation might trigger spatial cascades between apparently unrelated communities or ecological processes [START_REF] Harvey | Bridging ecology and conservation: From ecological networks to ecosystem function[END_REF]58], such as invasive fish affecting terrestrial arthropod communities [START_REF] Baxter | Fish invasion restructures stream and forest food webs by interrupting reciprocal prey subsidies[END_REF]. Overall, identifying the main paths of spatial flows between ecosystems, and the different scales at which these flows occur, is key to develop an empirically grounded theory of spatial ecology [START_REF] Massol | Linking community and ecosystem dynamics through spatial ecology[END_REF]. Notably, this requires incorporating organismal movement types other than dispersal into current theory, and accounting for their respective spatio-temporal scales (next section and Box 1).

A missing theory level

Current meta-ecosystem models have been built on simplest assumptions regarding among-ecosystem spatial flows to identify mechanisms general enough to apply to all systems [13, [START_REF] Gravel | Source and sink dynamics in meta-ecosystems[END_REF][START_REF] Gounand | Stability and complexity in model meta-ecosystems[END_REF][START_REF] Gravel | Patch dynamics, persistence, and species coexistence in metaecosystems[END_REF][START_REF] Marleau | Meta-ecosystem dynamics and functioning on finite spatial networks[END_REF][START_REF] Marleau | Emergence of nutrient co-limitation through movement in stoichiometric meta-ecosystems[END_REF]. However, model structures and parameterizations are too general to appropriately describe the nature of spatial couplings between real ecosystems. Moreover, equally limiting are empirical approaches based on system-centred models, which are useful for specific applications but lack generality and restrict any predictions to the focal system. To bridge this gap, an intermediate level of model generality must be found that would better capture the structure of ecosystem couplings by incorporating the main organismal movement types and their specific effects and scale in general meta-ecosystem models.

These spatial flows, at a first glance, look very specific to some systems: 'marine snow' made of phytoplankton and zooplankton's faecal pellets sinking from pelagic to benthic systems for instance [START_REF] Turner | Zooplankton fecal pellets, marine snow, phytodetritus and the ocean's biological pump[END_REF], or salmon carcasses fished by bears supporting productivity in lowland boreal forests [START_REF] Gende | Magnitude and fate of salmon-derived nutrients and energy in a coastal stream ecosystem[END_REF]. However, such seemingly specific spatial flows actually share similar functional roles. If we abandon a system-centred perspective to categorize spatial flows by type of effects they provoke in the recipient ecosystem, generalities can be drawn among contrasting systems.

For instance, marine snow corresponds to a directional detritus input from autotrophic to heterotrophic systems, for which analogues can be found in inland systems with forest litter inputs to stream benthos [START_REF] Wallace | Stream invertebrate productivity linked to forest subsidies: 37 stream-years of reference and experimental data[END_REF]. Similar functional equivalences are found between net cross-ecosystem resource flows triggered by bears bringing salmon carcasses on land, hippopotami grazing on savannahs and defecating in tropical rivers [START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF], or zooplankton feeding in pelagic areas during the night and excreting nutrient at daytime in the deep sea [START_REF] Stukel | Contributions of mesozooplankton to vertical carbon export in a coastal upwelling system[END_REF]. A commonality of all these cases is the daily net transport of inorganic nutrients or detritus triggered by foraging activity. Such generalizable functional roles of spatial flows are easily identified, incorporated into models, and contribute to build a process-based rather than system-based spatial framework.

Essential features of spatial flows to account for in a process-based spatial framework include consumer versus resource effects, timing (e.g. pulse frequency) and spatial scales. In that respect, the four organismal movement types described in Box 1 each display distinct characteristics (Figure 1C): consumer effects (i.e. demographic + consumption pressure) are the dominant result of dispersal, whereas, on a smaller spatial scale, resource effects dominate common life-cycle movements, such as with emerging aquatic midges or spawning salmon [START_REF] Gende | Magnitude and fate of salmon-derived nutrients and energy in a coastal stream ecosystem[END_REF][START_REF] Gratton | Ecosystem Linkages Between Lakes and the Surrounding Terrestrial Landscape in Northeast Iceland[END_REF]. Foraging and seasonal migrations trigger both consumption pressure in the recipient ecosystem and potentially net flows of resource, but act at local versus cross-continental scales, respectively, and on continuous versus pulsed time scales compared to local ecosystem dynamics (examples in Box 1). Overall, these ubiquitous movement types fulfil different functional roles in spatial dynamics, and their integration in models would capture essential characteristics of the spatial linkages between ecosystems, without impairing model generality by not being systemspecific. This effort of depicting more accurately the spatio-temporal and functional complexity of spatial couplings will move meta-ecosystem theory from abstraction to realistic generalities.

Challenges ahead for spatial ecology

Understanding the general effects of spatial flows on ecological dynamics is a first step.

Next, theory must identify realistic situations under which spatial flows matter for ecological dynamics by grounding models into empirical knowledge. Extensive data on spatial flows already exist across different fields of the empirical literature, but the broad picture is still missing. A major task is to quantify, unify, and synthetize data across fields.

We currently have only partial knowledge of what kind of flows connect ecosystems, with a bias toward data on resource flows which are easily measured by direct methods, such as passive trapping systems (e.g. terrestrial detritus inputs to freshwater systems, pelagic snow in oceans). In contrast, few studies document active flows of resources (but see [START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF][START_REF] Subalusky | Annual mass drownings of the Serengeti wildebeest migration influence nutrient cycling and storage in the Mara River[END_REF]), and dispersal is mostly analysed through its effect on populations or communities rather than estimated as a flow [START_REF] Jacobson | Quantifying and disentangling dispersal in metacommunities : how close have we come ? How far is there to go ?[END_REF]. Improving methods to track animal displacement [START_REF] Hussey | Aquatic animal telemetry: A panoramic window into the underwater world[END_REF][START_REF] Kays | Terrestrial animal tracking as an eye on life and planet[END_REF] will fill gaps in our knowledge of the spatial scales associated with organismal movement types, but we still have to quantify how much material is conveyed by these movements. When direct measurements are technically challenging, we need to develop and generalize indirect methods combining estimates of the diverse variables and processes involved in spatial flows, such as organisms' feeding and excretion rates, foraging behaviour, population densities in different habitats, average individual body mass, or dispersal kernels (see examples in [START_REF] Gratton | Flux of aquatic insect productivity to land: Comparison of lentic and lotic ecosystems[END_REF][START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF][START_REF] Subalusky | Annual mass drownings of the Serengeti wildebeest migration influence nutrient cycling and storage in the Mara River[END_REF]65]). New methods combining isotopic data on diet with consumption rates to estimate resource flows would similarly round out our knowledge of spatial flows.

Synthetizing local and spatial data in homogenized units will provide the material to determine which actual spatial flows drive natural meta-ecosystem dynamics and what the underlying drivers might be (i.e. organismal movement types or physical processes). As a minimum effort towards this necessary homogenization, we suggest consistently expressing spatial flows in areal units (per meter squared) of both donor and recipient ecosystems, in order to appropriately describe flows linking ecosystems of different sizes. We also need quantifications to be systematically associated with information on timing (i.e. duration and frequency) because whether spatial flows occur as continuous flows or pulses constricted in time (e.g. 15-cycle cicada outbreaks in central US [START_REF] Pray | Deposition and decomposition of periodical cicadas (Homoptera: Cicadidae: Magicicauda) in woodland aquatic ecosystems[END_REF]) could provoke contrasting effects on the stability of recipient ecosystem dynamics [START_REF] Sato | A test of the effects of timing of a pulsed resource subsidy on stream ecosystems[END_REF][START_REF] Reigada | Metapopulation Dynamics on Ephemeral Patches[END_REF][START_REF] Hastings | Temporally varying resources amplify the importance of resource input in ecological populations[END_REF].

Using models upgraded by this type of data synthesis to understand the relationship between spatial scales and dominant flow types will improve our ability to forecast changes in connected ecosystems (see Outstanding Questions Box for important aspects to address). This is essential for identifying the relevant spatial flows and species to maintain appropriate levels of connectivity and functioning in the landscape.

Conclusion

By linking contrasting spatial dynamics along a gradient of dispersal versus resource flows occurring at different scales, we propose a unified framework for spatial ecology. Our framework merges the static, but accurate, view of environmental heterogeneity proposed by landscape ecology with the dynamic view from meta-ecosystem theory. We believe this unification is a crucial step toward more fruitful exchanges between theory and empirical ecology. In particular, we advocate that incorporating a wider range of organismal movements into meta-ecosystem models will provide an appropriate balance between generality and realism when describing the Figure 1: Gradient of meta-ecosystem types and their spatial integration in the landscape. Natural meta-ecosystems fall along a gradient along which the pre-eminence of dispersal versus resource flows varies. At one end, (A) dispersal-based meta-ecosystems, displaying metacommunity like dynamics, connect distant ecosystem patches of similar habitat and functioning via dispersal (curved blue arrows), while at the other end, (B) resource-flow based metaecosystems connect adjacent ecosystems of radically different habitats via resource flows crossing their boundaries (horizontal straight black arrows). Resource flows might be driven by physical processes (e.g. gravity, wind or water currents) as well as biotic processes (e.g. life-cycle movement of organisms). (C) These two types of meta-ecosystem dynamics occur at different scales: dispersal can connect individual resource-flow based meta-ecosystems within landscapes. Foraging activities might trigger consumption pressure as well as transfer of matter between ecosystems within the landscape (vertical pink arrows), while seasonal migrations connect the landscape to more distant ecosystems (curved dotted orange arrow). Top panels (A-D) show the qualitative relationships between organism size, the distance crossed for a given type of movement, and the time spent relative to the organism's lifespan (inverse frequency). For instance, dispersal movement (A) or life-cycle migration (which corresponds to movement between contrasting habitats associated with specific ontogenic stages) (B) occur only once in a lifetime, whereas seasonal migration (C) occurs several times at regular intervals, and movement linked to foraging (D) occurs on a short time basis and multiple times during the life of an organism. The distance crossed during these movements might depend on the size and the mobility (e.g. flying ability) of the organisms, with larger or more mobile ones dispersing and migrating farther or foraging on larger spatial scales (A-D) (see [START_REF] Stevens | A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals[END_REF][START_REF] Jeltsch | Integrating movement ecology with biodiversity researchexploring new avenues to address spatiotemporal biodiversity dynamics[END_REF]). However, small organisms can also cross large distances through passive dispersal driven by air or water currents or larger organisms (A). Bottom diagrams (E-H) illustrate how spatial flows of an herbivore H (black arrows) driven by these different movement types might couple two ecosystems in which the herbivore feeds on a plant P, itself up-taking a resource R (grey boxes and arrows). In the meta-ecosystem framework, dispersal corresponds to movement among populations of the same species (E), whereas life-cycle movements represent added resources into the recipient ecosystem, since adults most often die immediately after reproducing (F). Dotted arrows denote that these flows are often as temporally-specific pulses. In seasonal migrations, pulsed flows can be of a magnitude that constitutes a shift in the recipient community structure compared to otherwise prevailing local dynamics (G). Lastly, foraging activity can constitute a net flow from an ecosystem to another, even if the animal uses the whole landscape and does not perceive it as a structured meta-ecosystem (H). I). Dispersal, which implies settlement away from an organism's place of birth [START_REF] Massol | How life-history traits affect ecosystem properties: effects of dispersal in meta-ecosystems[END_REF], essentially conveys consumer effects by adding individuals to the recipient ecosystem (Figure IA,E). The immigrants and their subsequent offspring, for instance Milu deer individuals re-colonizing Chinese forests [START_REF] Yang | Stepping-stones and dispersal flow: establishment of a metapopulation of Milu (Elaphurus davidianus) through natural re-wilding[END_REF], primarily exert a top-down pressure on local resources, even if their production of detritus might ultimately enrich recipient ecosystems. On the contrary, lifecycle movements, such as emerging aquatic insects moving to terrestrial systems for mating, oviposition and subsequent death, essentially convey resources by transporting individuals which do not settle or consume anything in the recipient ecosystem (Figure IB, F). For instance, clouds of midges emerging from arctic ponds can substantially enrich nearby tundras in nitrogen by their carcasses [START_REF] Gratton | Ecosystem Linkages Between Lakes and the Surrounding Terrestrial Landscape in Northeast Iceland[END_REF], and trigger bottom-up effects on local terrestrial herbivores [START_REF] Bultman | Terrestrial deposition of aquatic insects increases plant quality for insect herbivores and herbivore density[END_REF]. Seasonal migration and foraging movements involve both consumer and resource effects (Figure IC,D,G,H). They are distinct from one another by their spatio-temporal scales: Migratory animals couple ecosystems across biogeographic scales, for instance with geese feeding on crops in the Mississippi basin and breeding in arctic tundras [START_REF] Jefferies | Agricultural Food Subsidies, Migratory Connectivity and Large-Scale Disturbance in Arctic Coastal Systems: A Case Study[END_REF], while the foraging behaviour of large consumers can couple adjacent ecosystems, such as hippopotami grazing in savannahs and defecating in rivers [START_REF] Subalusky | The hippopotamus conveyor belt: vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub-Saharan Africa[END_REF]. For both types of movement, the asymmetry in animal activity constitutes a net flow of resource from one ecosystem to another (from feeding to breeding sites or grazing to resting sites) in addition to the consumption pressure organisms exert where they are. Finally, these movement types, and their effects, can interact across organismal scales: a large consumer, such as an elephant, might couple ecosystem resources through its foraging activity while searching for water points, and in parallel drive the dispersal of small aquatic organisms among ponds via the mud that cover its skin [START_REF] Vanschoenwinkel | Passive external transport of freshwater invertebrates by elephant and other mud-wallowing mammals in an African savannah habitat[END_REF]. It is so far theoretically unexplored how the co-occurrence of such contrasting but interacting spatial processes might affect ecosystem dynamics. Lastly, whether other movement types involving complex behaviours, such as mating aggregations or refuges from predation also commonly induce meta-ecosystem dynamics remains an open question. [72]; [START_REF] Viana | Migratory Birds as Global Dispersal Vectors[END_REF]75]; [START_REF] Courchamp | Mammal invaders on islands: impact, control and control impact[END_REF].

Table I. Effects and empirical illustrations of the different movement types.

Life-cycle migration

X

Midge emergence from arctic ponds enriching tundras in Iceland; salmon carcasses or shrimps subsidizing freshwater reproductive sites with marine nutrients.

[52]; [START_REF] Gende | Pacific salmon in aquatic and terrestrial ecosystems[END_REF] or [START_REF] Novak | Do upstream migrating, juvenile amphidromous shrimps, provide a marine subsidy to river ecosystems?[END_REF].

Seasonal migration

X X

Geese linking agrosystems in Mississippi and arctic tundras in Canada; all migratory birds and large herbivore herds transporting nutrients across African savannahs or boreal systems.

[73]; [START_REF] Bauer | Migratory animals couple biodiversity and ecosystem functioning worldwide[END_REF].

Foraging

X X

Hippopotami grazing in savannahs and pupping in the Mara river; seabirds bringing nutrients from the sea on islands; large marine mammal defecation or zooplankton vertical migrations transporting nutrients from pelagic to benthic systems.

[53]; [55]; [START_REF] Lavery | Iron defecation by sperm whales stimulates carbon export in the Southern Ocean[END_REF] or [START_REF] Stukel | Contributions of mesozooplankton to vertical carbon export in a coastal upwelling system[END_REF].

Box 2. Meta-ecosystem theory at the landscape scale

The original definition of a meta-ecosystem as "a set of ecosystems connected by spatial flows of energy, materials and organisms across ecosystem boundaries" [13] is general enough to apply to very different scales, from microbial (e.g. mites in soil [START_REF] Dong | The underlying processes of a soil mite metacommunity on a small scale[END_REF]) to biogeographic (e.g. temperate and arctic grasslands linked by migratory birds; [START_REF] Jefferies | Agricultural Food Subsidies, Migratory Connectivity and Large-Scale Disturbance in Arctic Coastal Systems: A Case Study[END_REF]). This flexibility, arising itself from the flexibility of the ecosystem concept, makes meta-ecosystems relevant to address questions on spatial dynamics between any coherent ecological entities linked by biotic or abiotic flows, with the scale varying with the organism or process of interest.

To link this concept and associated theory to empirical ecosystems-with the underlying motivation of facing the consequences of global changes-there is, however, a special interest in focusing on scales adapted to understand human impacts on ecosystems (habitat patches) and to link these spatial dynamics to large-scale processes such as global cycles. The landscape scale is especially interesting in that respect, because it encompasses the environmental heterogeneity produced by human activities, which fragment ecosystems into mosaics of patches (Figure I). These ecosystem patches of different habitat types often correspond to traditionally independent domains of ecology, such as river ecology, agriculture or forestry. By showing how spatial flows circulating within the landscape generate interdependencies between these different habitats, meta-ecosystem theory has a crucial role to play in identifying efficient actions to control the spread of local perturbations. Moreover, studying the impacts of spatial flows at the landscape scale provides tools to understand the spatial dynamics of regional biodiversity, but also to bridge it via ecological processes (productivity, recycling) to biogeochemical fluxes (carbon, water, nitrogen) at a larger scale (Figure I).

Figure I .

 I Figure I. Scales of organism movement types and effects on meta-ecosystem dynamics.Top panels (A-D) show the qualitative relationships between organism size, the distance crossed for a given type of movement, and the time spent relative to the organism's lifespan (inverse frequency). For instance, dispersal movement (A) or life-cycle migration (which corresponds to movement between contrasting habitats associated with specific ontogenic stages) (B) occur only once in a lifetime, whereas seasonal migration (C) occurs several times at regular intervals, and movement linked to foraging (D) occurs on a short time basis and multiple times during the life of an organism. The distance crossed during these movements might depend on the size and the mobility (e.g. flying ability) of the organisms, with larger or more mobile ones dispersing and migrating farther or foraging on larger spatial scales (A-D) (see[START_REF] Stevens | A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals[END_REF][START_REF] Jeltsch | Integrating movement ecology with biodiversity researchexploring new avenues to address spatiotemporal biodiversity dynamics[END_REF]). However, small organisms can also cross large distances through passive dispersal driven by air or water currents or larger organisms (A). Bottom diagrams (E-H) illustrate how spatial flows of an herbivore H (black arrows) driven by these different movement types might couple two ecosystems in which the herbivore feeds on a plant P, itself up-taking a resource R (grey boxes and arrows). In the meta-ecosystem framework, dispersal corresponds to movement among populations of the same species (E), whereas life-cycle movements represent added resources into the recipient ecosystem, since adults most often die immediately after reproducing (F). Dotted arrows denote that these flows are often as temporally-specific pulses. In seasonal migrations, pulsed flows can be of a magnitude that constitutes a shift in the recipient community structure compared to otherwise prevailing local dynamics (G). Lastly, foraging activity can constitute a net flow from an ecosystem to another, even if the animal uses the whole landscape and does not perceive it as a structured meta-ecosystem (H).

BoxesBox 1 .

 1 Feeding meta-ecosystem models with organismal movement typesMeta-ecosystem models explicitly consider only dispersal among the possible organismal movement types linking ecosystems. However, other common types of organismal movement are relevant for ecosystem couplings, such as life-cycle movement, seasonal migration, or foraging movement. When focusing on how the spatial flows resulting from organismal movements affect recipient ecosystem dynamics, we distinguish two contrasting types of effects: consumer and resource effects (Table

  of Milu (a species of deer) re-establishment in China; passive dispersal of plants and small aquatic organisms via birds; all kinds of biological invasions.

II. Spatial dynamics at the landscape scale.

  Both flows of dispersal (curve blue arrows) and resource (white arrows) coupled different types of habitat patches in the landscape, thereby inducing contrasting meta-ecosystem dynamics at regional and local scales respectively. Flows of resources can be driven by physical forces such as gravity, wind or water current (arrows 1), by animal movement such as insect emergence (arrows 2) or consumer foraging (arrow 3 next to a white bird), or by human transport (arrows 4). These flows affect biodiversity (e.g. denoted by a food web in a lake) and ecosystem processes (productivity, recycling), which themselves affect global cycles in different ways: example of the carbon cycle depicted by wide black arrows, with net carbon uptake by a forest patch and net carbon release to the atmosphere by a lake. Human populations benefit from ecosystem services provided by the landscape (right box), and human actions (left box) conducted at the landscape scale modulate biodiversity and ecosystem functioning, and ultimately biogeochemical cycles, which in turn induce the services.

		Dispersal (settlement)	Life-cycle migration (ontogeny -phenology) Biogeochemical cycles		Seasonal migration (phenology)		Foraging (daily functioning)
	(A)	Time	Photosynthesis	(B)	Time	Respiration	(C)	Time		(D)	Time
	Size-dependent dispersers	Plant, small organisms (movement via vectors)		Insects Amphib.			Insects / crabs
								Fish		(small)
											birds / fish
											(mobile)
			Distance				Distance			Distance		Distance
										mammals
										(large )
	Size				Size				Size			Size
	(E)	Ecosystem 1	Ecosystem 2		(F)	Ecosystem 1	Ecosystem 2	(G)	Ecosystem 1	Ecosystem 2	(H)	Ecosystem 1	Ecosystem 2
		H	H			H		H		H			H
		P	P			P		P		P	P		P	P
	R Biodiversity	R			R		R		R	R		R	R
													Biodiversity
									Actions
									Conservation,		Ecological processes
		Dispersal						ecological intensification,
		Resource flow						transport and agro-forestry,		Biogeochemical cycles
		Flux between landscape and atmosphere			energy management			Landscape
									Human populations	Ecosystem Services Food, water, material (wood),	Global scale
											recreation Air quality,		processes
											temperature
											and climate
											regulation

Shrimps

Figure

prominent features of couplings between ecosystems. While improving our understanding of spatial dynamics and their consequences for ecosystem functioning, our framework also helps refine relevant conservation targets and spatial scales.

C and R stand for consumer and resource effects on the recipient ecosystem, respectively. Consumer effects encompass demographic effects (population addition) and consumption pressure, while resource refers to transport of material serving as resources.

Acknowledgements

We thank Dries Bonte and the EMERGE working group for helpful discussions. We thank the editor, Dr. Paul Craze, and two anonymous reviewers for comments on a previous version of this manuscript. Funding is from the Swiss National Science Foundation Grant PP00P3_150698.

Glossary Box

Dispersal: 'the tendency of organisms to live, compete and reproduce away from their birth place' [START_REF] Massol | How life-history traits affect ecosystem properties: effects of dispersal in meta-ecosystems[END_REF].

Habitat: in this paper used as synonymous of 'biotope', that is a set of uniform environmental conditions.

Life-cycle-movement: cross-ecosystem movement occurring in an organism life to complete its ontogenic development.

Metacommunity: 'a set of local communities that are linked by dispersal of multiple potentially interacting species' [START_REF] Holyoak | Metacommunities: spatial dynamics and ecological communities[END_REF][START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF].

Meta-ecosystem: 'a set of ecosystems connected by spatial flows of energy, materials and organisms across ecosystem boundaries' [13].

Organismal movement: any behaviour that leads to the displacement of an organism from one place to another; here the organismal movement types considered are dispersal, life-cycle movement, foraging and seasonal migration (see Box 1).

Resource flow: spatial flow of inorganic nutrients, detritus or organisms dying, which constitute an increase of resource for the recipient community. Resource flow can be driven by passive physical processes or organismal movement (see Box 1).

Highlights

Cross-ecosystem movements drive landscape dynamics Among-ecosystem couplings are either dispersal-or resource-dominated Dispersal-based couplings occur at the regional scale between similar habitat types Resource-based couplings occur at the local scale between different habitat types

Outstanding Questions Box

How are different types of spatial flows distributed within landscapes? How do community structure and biodiversity affect the quality and quantity of cross-ecosystem flows?

How do perturbations influence cross-ecosystem flows? How can meta-ecosystem dynamics explain the spreading of perturbations between ecosystems? Which organisms play keystone roles in spatial dynamics, and how do they contribute to the spread of perturbations across ecosystems? How frequent are spatial cascades in natural landscapes?