Dr Lucy Currie

Dr Julio Fernandez-Cestau
email: j.fernandez-cestau@uea.ac.uk

Dr Luca Rocchigiani

Benoıt Bertrand

Simon J Lancaster

David L Hughes

Dr Helen Duckworth

Dr Saul T E Jones

Dan Credgington

Thomas J Penfold
email: tom.penfold@newcastle.ac.uk

Rocchigiani

Benoît Bertrand

T E Jones

Credgington

Manfred Bochmann
email: m.bochmann@uea.ac.uk

Prof. S B J Bertrand

Dr D L Lancaster

Prof M Hughes

Bochmann

Interactions Trigger and Control Switchable Photoemissions from Bimolecular Excited States

Keywords: coordination chemistry, gold, photoluminescence, stacking interactions, supramolecular assembly, tridentate ligands

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Introduction

Gold(III) complexes stabilized by cyclometalated pincer ligands have for some years been the focus of investigation because of their photophysical properties, and for applications as sensors, as bioimaging agents, and in organic light-emitting diodes. [1][2][3]13] Chelating CÀN bonded 2-aryl and 2,6-diaryl pyridine ligands have been found to be an effective means of stabilising Au III against reduction, [4] and in combination with strong carbon-based s-donor ligands, such as alkyl, aryl, alkynyl or N-heterocyclic carbenes (see structure A, where X = sdonor ligand), they show interesting photoluminescence in solution and the solid state under ambient conditions. [5][6][7][8][9][10] The photoemissions in these cases come from pincer-ligand-based triplet states.

For applications of gold(III) based luminophores the ability to modulate the emission wavelengths is of obvious importance. The most commonly applied strategies to achieve this are either the modification of the pincer ligand by more or less electron-withdrawing substituents, or the introduction of suitable substituents on the aryl or alkynyl ligands, or a combination of both. In either case, this approach requires significant synthetic effort. [5,6] Another general strategy, which has been applied particularly successfully for photoemissive Pt II complexes, is to utilize the tendency of square-planar complexes to form p-stacks which, in the case of platinum, involve metal-metal interactions and lead to concentration-dependent red-shifts. [11][12][13] However, although Au III and Pt II are isoelectronic and adopt the same square-planar coordination geometry, for Au III pincer complexes there is little evidence for metal-metal interaction. [START_REF]A relatively close contact of 3.495 has been reported in a crystalline Au III pincer complex and interpreted as an Au•••Au interaction; however[END_REF] As we have recently shown, [15] replacing the pyridine ring in C^N^C pincer ligands by pyrazine (structure B) has several important consequences: firstly, the photoluminescence intensity increases, often dramatically, and secondly, strong s-donor ligands X are no longer required to generate luminescent complexes. The p* level in pyrazine is around 0.95 eV lower than in pyridine, [16] which enhances the p-acceptor characteristics in LLCT and MLCT processes. A much wider range of X ligands will therefore lead to photoemissive compounds.

We now find that pyrazine complexes of type B where X = alkyl or aryl thiolate form a new class of photoluminescent complexes, with intriguing properties. Unlike previously reported gold(III) thiolates, several of these compounds show strong photoluminescence at room temperature in the solid state, in solution, and in a polymer matrix. Rather unexpectedly, some complexes form two distinct crystal polymorphs, one of which is strongly emissive at room temperature while the other is not, apparently depending on the packing and intermolecular interactions in the crystal. In fact, it turns out that only certain specific types of p-stacking produce photoemission, and that this photoemission occurs mainly from a bimolecular excited state. Furthermore, these complexes demonstrate how p-interactions control both the luminescence pathway and the emis-sion wavelengths, from green to red. In particular, it proved possible to switch reversibly between red and green emissions.

Results and Discussion

Synthesis and characterization

In contrast to the plethora of known Au I thiolato complexes, [START_REF] Gimeno | Comprehensive Coordination Chemistry II[END_REF] gold(III) thiolates are comparatively rare. Structurally characterized examples include compounds of the type [X 2 Au(m-SR)] 2 (X = Cl, Me; R = alkyl, aryl), [START_REF] Chen | Gold(III) thiolates with bridging thiolato ligands: a)[END_REF] tetrathiolato anions [Au(SR) 4 ] À (R = aryl or fluoroaryl), [19] polynuclear Au I /Au III compounds, [20] and Au III thiolates supported by cyclometallated C^N and C^N^N ligands. [21] None of these are known to be photoluminescent. A thiopyridine pincer complex, [Au(C^N py ^C)(S-2-py)], (A, X= 2-thiopyridine), was described as only weakly emissive at 77 K and non-emissive at room temperature. [22] One reason for the relative rarity of Au III thiolates is that in most cases thiols act as reducing agents to give gold(I) products; this was shown to occur, for example, even for N^N^N-type Au III pincer complexes. [23] However, the complexes 1-8 reported herein proved stable to reduction by thiols and are easily accessible with very good yields by treating (C^N pz ^C)AuCl with the thiol in acetone in the presence of K 2 CO 3 (Scheme 1). The analogous pyridine complex (C^N py ^C)AuSNp-2 ( 9) was prepared similarly for comparison.

Solid-state structure and crystal packing

Slow evaporation of a solution of (C^N pz ^C)AuSPh 1 in a dichloromethane/Pr i OH mixture (9:1 v/v) produced two different types of crystals, red plates (1 red, with two independent molecules in the unit cell) together with yellow blocks (1 yellow). The main difference in molecular geometries between the red and yellow crystal forms are the torsion angles between the AuÀSÀC and S-phenyl planes, which are 50. 1(4) and58.2(3)8 for the two molecules in the red form of 1, and 94. 3(6)8 in the yellow form (Figure 1). Similarly, the naphthalene thiolate compound (C^N pz ^C)AuSNp-2 (5) also gave a mixture of red and yellow crystals (5 red and 5 yellow, respectively; Figure 2). Here the torsion angles between the coordination plane of gold and the S-naphtyl plane in the two polymorphs are quite similar, namely 52.3(5)8 (red) and 56.5(3)8 (yellow). In none of these cases was this variation in crystal habit due to the inclusion of solvent molecules; the red and yellow modifications are therefore true polymorphs rather than different solvate structures.

The X-ray structures of (C^N pz ^C)AuSC 6 H 4 tBu-4 2, (C^N pz ^C)AuSC 5 H 4 N-2 3, (C^N pz ^C)AuS-4-methylcoumarin (7), and (C^N py ^C)AuSNp-2•CH 2 Cl 2 (9•CH 2 Cl 2 ) were also determined (see the Supporting Information). While 2 as a powder was yellow-orange, indicating a mixture of isomers, only the Scheme 1. Synthesis of gold(III) thiolates, including the numbering scheme used for NMR assignments. Left: Structure of (C^N pz ^C)AuSNp-2 (5 red). [28] Ellipsoids set at 50 % probability; H atoms omitted for clarity. Selected bond distances [] and angles [8]: Au1ÀS1 2.263(2), Au1ÀN1 2.006(6), Au1ÀC6 2.069(6), Au1À C16 2.080(6); N1-Au1-S1 174. yellow crystals were suitable for X-ray diffraction. Compounds 3 and 7 also formed yellow crystals. The 1-naphthylthiolate 4 and the quinoline complex 6 were obtained as orange solids, while the adamantyl complex 8 was red; none gave crystals suitable for X-ray diffraction. The only compound that crystallised with a solvent molecule, CH 2 Cl 2 , was the pyridine-based complex 9.

The red and yellow crystal forms of 1 and 5 show dramatic differences in their photophysical properties: the red modifications are strongly luminescent, while the yellow crystals are non-emissive at room temperature (Supporting Information, Figure S25). In the absence of compositional variation, the origin of these differences must be due to molecular interactions in the solid state.

The packing of the red and yellow forms of 1 is shown in Figure 3. Molecule A of 1 red consists of dimers formed by a head-to-head p-interaction with pyrazine rings of neighbouring molecules adopting a "parallel offset" orientation (see the Supporting Information for the slightly different packing of molecule B). [24] The distance between the planes of the pincer ligands in these dimers is relatively short, 3.306 . By contrast, 1 yellow forms one-dimensional stacks in which the pyrazine ring of one molecule is superimposed on the AuÀS bond of its nearest neighbours. The o-phenyl protons of the PhS ligand of one molecule interact with the two pyrazine-N atoms of its neighbours forming an H•••N(pz)•••H rod-type arrangement. The separation between molecular planes in 1 yellow that this stacking produces is much wider than in 1 red, 3.472 .

Similarly, the polymorph 5 red shows the same type of pyrazine•••pyrazine p-interactions as 1 red and consists of p-stacked dimers with short intermolecular distances of only 3.235 . By contrast, the yellow isomer of this complex (5 yellow) crystallizes as head-to-tail rods, characterised by S•••p interactions and a much wider inter-plane spacing of 3.482 .

The yellow crystal polymorph of (C^N pz ^C)AuSC 6 H 4 tBu-4 (2) is also based on dimers formed through parallel offset p-p interactions, but in this case the pyrazine ring of one molecule is aligned with one of the AuÀC bonds of a second molecule and vice versa, with an inter-plane distance of 3.469 . In the nonemissive pyridine complex 9, a phenylpyridine moiety of one molecule is aligned with the corresponding moiety of a neighbouring molecule but avoids any direct pyridine-pyridine pstacking.

In the 2-thiopyridine complex (C^N pz ^C)AuSC 5 H 4 N-2 (3) a particular case of aggregation is found as the presence of the pyridine-N atom generates intermolecular H•••N•••H interactions with protons of a neighbouring molecule and inter-plane distance of 3.410 .

Overall, it is evident that the red polymorphs are based on pyrazine•••pyrazine p-interactions with short inter-plane distances of 3.2-3.3 , whereas in the yellow modifications the pyrazine ring of one molecule is aligned with either an AuÀS bond or a phenyl ring of a second molecule, with wider p-p distances of > 3.4 . There are no significant differences in the intramolecular distances and angles. All structures show the expected coordination environment around Au, except for the N-Au-S units which deviate significantly from linearity, with N-Au-S angles ranging from 170.7-175.78. This effect is most probably due to steric repulsion between the tBu substituents of the pincer ligand and the thiolate ligands; in S-pyridyl complex the intramolecular N•••H interactions may be an additional distorting factor.

Intermolecular interactions in solution

To understand whether such interactions persist in solution, the self-aggregation tendency of 1 and 5 was explored by means of NMR spectroscopy. In particular, we investigated the concentration dependence of both the 1 H NMR spectrum and the self-diffusion coefficient (D t ) in CD 2 Cl 2 .

The results indicate that chemical shift values of both 1 and 5 are only marginally affected by increasing the concentration from 10 À4 to over 10 À2 m (see the Supporting Information). Protons H2 and H5 (for numbering see Scheme 1) showed the highest variability, which suggests the presence of head-tohead interactions arising from p-stacking of two pyrazine rings, as was observed in the solid state. In agreement with this assumption, the chemical shift values of the thiolate moieties were almost independent of concentration. By imposing a monomer-dimer equilibrium model, we derived rather small values of average dimerization constants K dim = 1.0 and 1.8 m À1 for 1 and 5, respectively, suggesting that the concentration of p-stacked dimers in solution is low. This method suggests mol fractions of dimers of around 10 mol % at [1] = 55.5 mm, and about 18 mol % in the case of [5] = 72.5 mm.

As independent confirmation of this association, self-diffusion coefficient values D t of 1 and 5 were determined by diffusion NMR techniques [25] (see the Supporting Information for details). From the D t values, the structural parameters P were derived by approximating the shapes of 1 and 5 to an oblate ellipsoid and plotted against the concentration. Also, in this case the P values were found to be slightly dependent on the concentration, as expected for a weak self-aggregation process. In good agreement with the interpolation of the chemical shift data, the concentration of dimers rises from 2 mol % at [1] = 2.9 mm to 21 mol % at [1] = 55.5 mm. Compound 5 showed a similar trend, with 2 % dimers at 0.6 mm, rising to 26 % at 72.5 mm (Supporting Information, Table S3.1). The chloride (C^N Pz ^C)AuCl was included in these measurements for comparison; it shows a similar tendency towards p-stacks in solution, which demonstrates that this aggregation is connected with the pincer structure, not the thiolate ligands.

Photophysical properties

The photophysical properties of these complexes depend on the structures of the crystal polymorphs in the solid state and on the formation of p-stacked aggregates in solution.

The UV/Vis absorption spectra of 1-9 show the expected pattern, with the typical low energy bands characterized by the vibronic progression of the C^N^C pincer ligands which are attributed to metal-perturbed 1 IL(C^N^C) transitions.

As is illustrated in Figure 4, in the pyridine complex (C^N py ^C)AuSNp-2 ( 9) this low energy band is significantly blue-shifted relative to the pyrazine analogue 5; this shift reflects the fact that the p* level in pyrazine is 0.95 eV lower than in pyridine. [16] A comparison of the spectra of (C^N pz ^C)AuCl and 5 suggests that thiolato ligands slightly increase this p-p* energy gap.

Absorption maxima in the 420-440 nm region of the thiolates disagree with the observed deep-red colour of the pyrazine-based thiolate complexes in dichloromethane solution. The deep colour can however be explained by the weak broad absorption tail in the 470-530 nm region, (e % 1-2 10 3 m À1 cm À1 ; see the Supporting Information). Given the results of the NMR experiments, these low energy bands are most probably a reflection of molecular aggregation in solution.

The complexes show photoluminescence both in solution and in the solid state. The luminescence properties can be rationalized on the basis of two general emissive pathways: a green-centred emission, characterized by the vibronic progression due to the C=N stretching mode of the pincer ligand, of a type seen for many such C^N^C pincer complexes and attributed to a 3 IL(C^N^C) transition, [5,6] and a second, lowerenergy unstructured red emission band at > 600 nm.

In the solid state at room temperature the complexes of Nheterocyclic aryl thiolates, that is, (C^N pz ^C)AuSC 5 H 4 N-2 ( 3) and (C^N pz ^C)AuSQuin-2 ( 6) are weakly to non-emissive, but on cooling to 77 K they show a green emission spectrum with vibronic structure. In solution and in PMMA matrix relatively weak green emissions are observed even at 298 K (Supporting Information, Tables S4.2 andS4.3). The same behaviour is seen for the 2,6-diphenylpyridine pincer complex 9, which is emissive only at 77 K, with the 3 IL(C^N py ^C) band showing the typical structured profile with l em max = 517 nm. Nevertheless, compounds 3, 6, and 9 are the exceptions in this series of thiolato complexes given that complexes with aryl thiolates without hetero-ring atoms (1, 2, 4, and 5), as well as the alkyl based 1-adamantylthiolate 8, all show emissions dominated by unstructured red low-energy bands. Even more striking is the analysis of the excitation bands in both cases. While in 9 the excitation at 77 K resembles the absorption spectrum at 298 K, in the case of 5, the emission at 298 K is associated with a fairly sharp low-energy excitation band at l ex = 540 nm. This is also evident in the increase in emission quantum yield with increasing excitation wavelength, which reflects the shape of the excitation band. This points to the association of the red emission with the low-energy tail of more than 500 nm observed in the UV/vis spectrum; this band is therefore most probably connected with the pyrazine p-aggregation process described above. The two types of emissive behaviour are illustrated in Figure 5 for compounds 5 and 9.

The relationship of the red emission with the type of pstacking is further supported by the very different solid-state photoluminescence properties of the red and yellow crystal polymorphs 5 red and 5 yellow. While crystals of 5 red show unstructured emission bands both at 298 and 77 K (l max 650 nm at 298 K, 653 nm at 77 K), 5 yellow is non-emissive at 298 K, while at 77 K only the structured green 3 IL(C^N pz ^C) emission band is observed. Evidently the complex profile of the emission band of 5 isolated as a powder from the reaction mixture prior to recrystallization is the result of the sum of these two components (Figure 6 a).

The two different emission pathways are also evident in solution and depend on concentration, temperature, and the solvent. For example, a 10 À5 m dichloromethane solution of 5 shows the green triplet emission, which changes to red as the concentration increases (Figure 6 b). Another factor is the solvent: 10 À2 m dichloromethane solutions of 2 and 5 at 77 K show red emissions, while in frozen 2-methyl-THF at the same concentration and temperature green structured bands are observed. Even for the same sample the emission characteristics can be switched. A particularly striking case for the change of emission pathway as a function of sample history is provided by solutions of 2 (Figure 7). In frozen dichloromethane at 77 K complex 2 (10 À2 m) produces the expected red emission; however, warming to just above the melting point of the solvent followed by refreezing leads to a mixed emission, orange to the naked eye. Repeating this cycle a number of times generates predominantly the structured green 3 IL(C^N pz ^C) emission, which on warming to 298 K reverts to red. All these findings confirm that the origin of the red emission is the p-aggregation of the pyrazine-based pincer ligands. Clearly the thawfreeze treatment of the solution affects the concentration of pstacked dimers and influences intermolecular interactions and the emission mode.

The complexes show a similar aggregation tendency when embedded in a polymer matrix, poly(methylmethacrylate) (PMMA). Varying the concentrations in PMMA demonstrates the fundamental differences in the green and red emission pathways. The photoluminescence quantum yield (PLQY) of the blue-green emitting quinolinyl thiolate complex 6 decreases with increasing concentration; by contrast, complex 5 shows an increase in PLQY from F PL < 0.01 at 1 wt % loading to F PL = 0.11 at 50wt % loading, together with a slight red-shift (Figure 8; Supporting Information, Tables S4.3, S4.4). The behaviour of 6 resembles the concentration-dependent quenching observed for the classical triplet emitter, Ir(ppy) 3 . [26] The emission of 5, by contrast, shows aggregation-induced PL enhancement. [START_REF]In the present case, individual molecules will show triplet emission. This process differs therefore from the well-recognized aggregation-induced enhancement (AIE) where aggregation limits molecular mobility[END_REF] The same trends in the emissions of 5 and 6 were observed in other polymers, such as polystyrene and PVK (polyvinyl carbazole).

Further information about the nature of the red emission is provided by the excitation lifetimes. The aggregation-induced red bands in the solid state and in a PMMA polymer matrix decay following bi-exponential functions (Supporting Information, Table S4.3); this was investigated in detail in the case of complex 5.

The red band of 5 in PMMA is the result of two different components; a prompt emission with a short lifetime of 25-30 ns, which is assigned to photoexcited singlet states, and a delayed component with a temperature-dependent lifetime in the range of 0.3-20 ms (Figure 9). The emission band measured after 200-250 ns shows the same profile as the prompt emission (Supporting Information, Figure S35), suggesting that both emissions originate from states with similar energies. Despite the negligible effect of temperature on the emission wavelength, there is a strong dependence of the lifetime of the long-lived component on temperature, with characteristic activation energy of 73 AE 5 meV. Since this longer-lived state constitutes the major emission component, there is a marked drop in luminescence intensity with increasing temperature (Supporting Information, Table S4.8 and Figure S36). This sug- In contrast to the behaviour in PMMA films, in solution at room temperature the red emission shows only the prompt mono-exponential decay, with a lifetime of 28 ns, suggesting the long-lived triplet-mediated emission component is fully quenched. In glassy conditions (solutions frozen to 77 K) the same effect as in polymeric matrix is observed, that is, the long-lived emission recovers, with lifetime in the ms regime.

Theoretical calculations

To support the analysis of the experimental data we have performed density functional (DFT) and time-dependent density functional theory (TDDFT) calculations with a particular focus upon providing insight into the influence of the type and magnitude of the supramolecular interactions in the photophysical properties of the complexes. Details of the calculations can be found in the Supporting Information. Complex 5 was selected as model for all the complexes. Specifically, three different structural dispositions have been optimised: an isolated molecule (5 monomer), a dimer based on the pz-pz aggregation of the red crystal (5 reddimer), and a dimer based on the S-pz aggregation of the yellow form (5 yellowdimer).

In all cases, the optimized structures agree with the X-ray analysis (Supporting Information, Table S5). This is not only illustrated by the intramolecular distances but also the distances between gold coordination planes, which fit the observed supramolecular arrangements in both dimers (5 reddimer: inter-plane distance 3.26 (calculated) vs. 3.235 (X-ray); 5 yellowdimer: 3.45 vs. 3.482 (X-ray)). The frontier orbitals of these limit situations show a particular relevant feature: whereas in 5 reddimer the HOMO is mainly located on one thiolate ligands, the LUMO is delocalized over both pyrazine ligands (Figure 10). It is this bimolecular MO that is responsible for the intense, unstructured red photoemission.

By contrast, in 5 yellowdimer the LUMO is located on the cyclometallated ligand of the same molecule, without delocalization over both molecules of the dimer. Very similar frontier orbitals are found in 5 monomer, in line with the concept that the photophysical properties of the yellow polymorphs are based on isolated molecules, not supramolecular aggregation.

The calculated emission energy for the optimised triplet geof 5 reddimer is 1.76 eV (704 nm, oscillator strength 2 10 À4 ) which is in agreement with the red emission discussed above. In contrast, for 5 yellowdimer and 5 monomer the energy of this transition is very low (> 850 nm), so the non-radiative emission rate will be large. The calculations suggest that the emission does not arise from this lowest triplet state but from the second triplet T 2 (l em (calcd) = 573 nm for 5 monomer and 585 nm for 5 yellowdimer). This transition is mainly HOMOÀ2!LUMO, and the nature of these orbitals confirms the 3 p-p*(C^N pz ^C) parentage.

The theoretical calculations support our interpretation of the experimental results. The green, structured emission has

Conclusion

In summary, gold(III) thiolato complexes based on cyclometallated pyrazine-centred pincer ligands form a new class of photoluminescent gold compounds, which are characterized by the remarkable dependence of their luminescence behaviour on the type of supramolecular arrangement present in the solid and in solution. Crystals of the aryl thiolates (C^N pz ^C)AuSR (R = Ph, 2-naphthyl) exhibit polymorphism and consist of a red, strongly photoemissive phase, and a yellow modification, which is non-emissive at room temperature. The deep red colour is associated with the formation of head-tohead p-stacked dimers in which pyrazine rings of neighbouring molecules adopt a "parallel offset" orientation. This disposition shows short inter-plane distances of 3.2-3.3 , compared to about 3.4 in the non-emissive modifications. NMR and UV/ Vis spectroscopy confirmed that similar p-stacked dimers are also present in solution; this explains the concentration-dependent emission behaviour of these compounds. Changes in the p-interactions in solution, which can be brought about by freeze-thaw cycles, allow the emission characteristics to be switched reversibly between red and green.

While isolated molecules at 77 K show the typical green, vibronically structured emissions of ligand-based 3 IL(C^N^C) triplet transitions, calculations show that dimerization through pyrazine•••pyrazine interactions produces a LUMO delocalized over the supramolecular p-dimer. This MO is involved in the bimolecular excited state and is mainly responsible for the observed red 3 LLCT(SR!(C^N pz ^C) 2 ) emission. While in PMMA matrix the green 3 IL(C^N^C) emission is subject to concentration quenching, the photoluminescence quantum yield of the red emission increases with increasing concentration. To the best of our knowledge this is first detailed study of aggregation-enhanced emission in Au III chemistry.

Figure 1 .

 1 Figure1. Structure of (C^N pz ^C)AuSPh (1 red).[28] Ellipsoids set at 50 % probability; H atoms omitted for clarity. Selected bond distances [] and angles [8]: Molecule A: Au1ÀS1 2.273(1), Au1ÀN11 2.006(3), Au1ÀC122 2.086(4), Au1ÀC162 2.091(4); N11-Au1-S1 175.66(8), C122-Au1-N11 80.5(1), C162-Au1-N11 80.6(1), C122-Au1-S1 95.46(9), C162-Au1-S1 103.38(9), Au1-S1-C171 110.2(2), torsion Au1-S1-C171-C172 50.1(4), Molecule B: Au2ÀS2 2.277(1), Au2ÀN21 2.017(4), Au2À C222 2.083(4); N21-Au2-S2 172.2(1), C222-Au2-N21 80.6(1), C262-Au2-N21 80.1(1), C222-Au2-S2 91.8(1), C262-Au2-S2 107.49(9), Au2-S2-C271 110.9(1), torsion Au2-S2-C271-C272 58.2(3). 1 yellow: Au1ÀS7 2.276(2), Au1ÀN1 2.022(6), Au1ÀC22 2.088(7), Au1ÀC62 2.080(7); N1-Au1-S7 170.94(18), C22-Au1-N1 81.3(3), C62-Au1-N1 79.8(3), C22-Au1-S7 89.6(2), C62-Au1-S7 109.2(2), Au1-S7-C71 116.6(3), torsion Au1-S7-C71-C72 94.3(6).

  Figure1. Structure of (C^N pz ^C)AuSPh (1 red).[28] Ellipsoids set at 50 % probability; H atoms omitted for clarity. Selected bond distances [] and angles [8]: Molecule A: Au1ÀS1 2.273(1), Au1ÀN11 2.006(3), Au1ÀC122 2.086(4), Au1ÀC162 2.091(4); N11-Au1-S1 175.66(8), C122-Au1-N11 80.5(1), C162-Au1-N11 80.6(1), C122-Au1-S1 95.46(9), C162-Au1-S1 103.38(9), Au1-S1-C171 110.2(2), torsion Au1-S1-C171-C172 50.1(4), Molecule B: Au2ÀS2 2.277(1), Au2ÀN21 2.017(4), Au2À C222 2.083(4); N21-Au2-S2 172.2(1), C222-Au2-N21 80.6(1), C262-Au2-N21 80.1(1), C222-Au2-S2 91.8(1), C262-Au2-S2 107.49(9), Au2-S2-C271 110.9(1), torsion Au2-S2-C271-C272 58.2(3). 1 yellow: Au1ÀS7 2.276(2), Au1ÀN1 2.022(6), Au1ÀC22 2.088(7), Au1ÀC62 2.080(7); N1-Au1-S7 170.94(18), C22-Au1-N1 81.3(3), C62-Au1-N1 79.8(3), C22-Au1-S7 89.6(2), C62-Au1-S7 109.2(2), Au1-S7-C71 116.6(3), torsion Au1-S7-C71-C72 94.3(6).

Figure 2 .

 2 Figure2. Left: Structure of (C^N pz ^C)AuSNp-2 (5 red).[28] Ellipsoids set at 50 % probability; H atoms omitted for clarity. Selected bond distances [] andangles [8]: Au1ÀS1 2.263(2), Au1ÀN1 2.006(6), Au1ÀC6 2.069(6), Au1À C16 2.080(6); N1-Au1-S1 174.3(2), C6-Au1-N1 80.6(3), C16-Au1-N1 80.1(2), C6-Au1-S1 93.8(2), C16-Au1-S1 105.6(2), Au1-S1-C25 109.5(2), torsion Au1-S1-C25-C34 52.3(5). Right: 5 yellow: Au1ÀS1 2.2837(8), Au1ÀN1 2.014(3), Au1À C6 2.095(3), Au1ÀC16 2.086(3); N1-Au1-S1 174.05(7), C6-Au1-N1 80.7(1), C16-Au1-N1 80.2(1), C6-Au1-S1 104.68(8), C16-Au1-S1 94.53(8), Au1-S1-C25 109.8(1), torsion Au1-S1-C25-C34 56.5(3).

  Figure2. Left: Structure of (C^N pz ^C)AuSNp-2 (5 red).[28] Ellipsoids set at 50 % probability; H atoms omitted for clarity. Selected bond distances [] andangles [8]: Au1ÀS1 2.263(2), Au1ÀN1 2.006(6), Au1ÀC6 2.069(6), Au1À C16 2.080(6); N1-Au1-S1 174.3(2), C6-Au1-N1 80.6(3), C16-Au1-N1 80.1(2), C6-Au1-S1 93.8(2), C16-Au1-S1 105.6(2), Au1-S1-C25 109.5(2), torsion Au1-S1-C25-C34 52.3(5). Right: 5 yellow: Au1ÀS1 2.2837(8), Au1ÀN1 2.014(3), Au1À C6 2.095(3), Au1ÀC16 2.086(3); N1-Au1-S1 174.05(7), C6-Au1-N1 80.7(1), C16-Au1-N1 80.2(1), C6-Au1-S1 104.68(8), C16-Au1-S1 94.53(8), Au1-S1-C25 109.8(1), torsion Au1-S1-C25-C34 56.5(3).

Figure 3 .

 3 Figure 3. Intermolecular packing in emissive (red) and non-emissive (yellow) polymorphs of gold(III) thiolates, showing the pz•••pz stacking of the red and pz•••AuÀS alignment of the yellow forms (pz = pyrazine). The inter-plane distance is measured between coordination planes of Au defined by Au, N, and C (bonded to Au). The packing diagrams of 1 yellow and 3 also show CH•••N close contacts (< 2.6 ).

Figure 4 .

 4 Figure 4. Comparison between the UV/Vis absorption spectra of (C^N pz ^C)AuCl, (C^N pz ^C)AuSNp-2 (5), and (C^N py ^C)AuSNp-2 (9) in CH 2 Cl 2 (5 10 À5 m).

Figure 5 .

 5 Figure 5. Top: Emission of 5 at 298 K (grey solid line; l ex 540 nm), excitation band at 298 K (grey dotted line), and emission at 77 K (black solid line; l ex 490 nm) (CH 2 Cl 2 , 10 À4 m). Bottom: Emission of 9 at 77 K (l ex 400 nm; black solid line) and excitation band at 77 K (black dotted line; CH 2 Cl 2 , 10 À4 m).

Figure 6 .

 6 Figure 6. a) Photoemission spectra of crystalline samples of 5 red (black solid line) and 5 yellow (grey solid line) and the crude orange solid 5 (black dotted line) at 77 K. b) Normalized photoemission spectra of 5 in CH 2 Cl 2 at different concentrations.

Figure 7 .

 7 Figure 7. Changes in the emission spectra of CH 2 Cl 2 solutions of 2 (10 À2 m) with sample treatment. a) Initial spectrum at 77 K; b) spectrum after thawing at 170 K followed by cooling to 77 K; c) spectrum of 2 at 77 K after four freeze-thaw-freeze cycles.

Figure 8 .

 8 Figure 8. Changes in photoluminescence intensity as a function of concentration, showing the increase for 5 (red) and the decrease for 6 (green) in PMMA matrices with 5-50 wt % À1 loading (298 K). Chem. Eur. J. 2016, 22, 1 -10

3 IL(

 3 C^N^C) character and is based on isolated molecules; however, aggregation through pyrazine•••pyrazine p-interactions, and only through pyrazine•••pyrazine p-interactions, induces the appearance of a second emissive pathway. The origin for this emission is most probably charge-transfer transitions from the thiolate to the p-stacked dimeric pyrazine moiety, 3 LLCT(SR!{(C^N pz ^C) 2 }). The presence of two components in rigid media (frozen solution or polymer matrix) and a single, red-shifted, emission in solution can be rationalized on the basis of two emissive states of very similar energy, which are able to relax in fluid conditions to a unique lower energy state.

Figure 9 .

 9 Figure 9. Variation of the excitation lifetime of the long-lived component of 5 red in (10 wt % À1 in PMMA film) at different temperatures.

Figure 10 .

 10 Figure 10. HOMO and LUMO frontier orbitals for 5 yellowdimer and 5 reddimer.

Chem. Eur. J. 2016, 22, 1 -10 www.chemeurj.org 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Chem. Eur. J. 2016, 22, 1 -10 www.chemeurj.org 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 4 &&

Chem. Eur. J. 2016, 22, 1 -10 www.chemeurj.org 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 6 &&

Chem. Eur. J. 2016, 22, 1 -10 www.chemeurj.org 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 8 &&

Chem. Eur. J. 2016, 22, 1 -10 www.chemeurj.org 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 10 &&

Acknowledgements

This work was supported by the European Research Council. M.B. is an ERC Advanced Investigator Award holder (grant no. 338944-GOCAT). D.C. and S.J. acknowledge the Royal Society for financial support. We are grateful to the EPSRC National Crystallographic Service, Southampton, UK, for collection of crystallographic data sets. [29] We thank the Centro de Investigación en Síntesis Química, Universidad de La Rioja, E-26006, LogroÇo, Spain, and especially Professor E. Lalinde, for their kind assistance with the determination of the PL properties of the complexes.

Luminescent Supramolecular Assemblies

Gold(III) thiolates supported by pyrazine-based C^N^C pincer ligands show a variable photoluminescent behaviour that depends strongly on the type of pyrazine p-stacking. Different types of p-stacking lead to crystal polymorphs, but are also present in solution. Red emissions occur from a dimeric excited state, while monomeric or weakly stacked molecules give green emissions. The image illustrates that this p-stacking is reversible, so that the emissions can switch between red and green. For more details, see the Full Paper by Fernandez-Cestau, Credgington, Penfold, Bochmann, et al. on page && ff.


Laurence Gaume
email: laurence.gaume@cirad.fr

Marion Desquilbet

InsectChange: Comment

Keywords: Insects, Terrestrial invertebrates, Freshwater invertebrates, Insect abundance change, Insect decline, Time-series analysis, Methodological biases, Cropland cover assessment, Land cover analysis

Introduction

Today experts agree that biodiversity is shrinking in the face of global changes characterizing the Anthropocene era. However, as regards insects, which provide invaluable ecosystem services, decline rates are still debated, largely because their assessment is hampered by lack of data and subsequent analytical weaknesses. There is also no consensus on the main drivers of their change, including land use (urbanization/agriculture), climate, pesticides, pollution and invasive species, mostly because these drivers are not easily disentangled or may act in synergy [START_REF] Wagner | Insect decline in the Anthropocene: Death by a thousand cuts[END_REF].

While many authors have warned on insect extinctions worldwide [START_REF] Cardoso | Scientists' warning to humanity on insect extinctions[END_REF], van Klink et al. (2020a) added to the debate by estimating a smaller decline in the abundance of terrestrial insects than reported by these authors and an increase in freshwater insects. They found that cropland was not involved in terrestrial insect decline and proposed improved water quality to be a driver of the freshwater insect increase. Yet their meta-analysis raised comments regarding their data selection and methodology (Desquilbet et al. 2020) leading to some corrections (van Klink et al. 2020b), the limitations of abundance and biomass as sole indicators of insect trends [START_REF] Jähnig | Revisiting global trends in freshwater insect biodiversity[END_REF]) and the heterogeneity in temporal coverage with a lack of old baselines [START_REF] Duchenne | Controversy over the decline of arthropods: a matter of temporal baseline? Peer Community Journal 2[END_REF]). In the first place, however, the sensitive issue of insect trends and drivers requires the utmost rigor in databases intended to serve as references.

We present an in-depth study of the InsectChange database [START_REF] Van Klink | InsectChange: a global database of temporal changes in insect and arachnid assemblages[END_REF] underlying the analysis by van Klink et al. (2020a). InsectChange compiles time series of abundance and biomass of invertebrates reported as insects and arachnids, as well as ecological data likely to influence their trends. Based on a comprehensive review of the original publications and datasets, we highlight numerous data limitations, whose accumulation also biases any assessment of insect change and drivers of change from this database.

The different issues in the InsectChange database

A first shortcoming is a lack of clear information on which invertebrate taxa are included in InsectChange. These are insects and arachnids according to the title and main text, but also entognaths, as indicated in the keywords and appendices. Considering entognaths within the scope of the data paper, we find that 17 types of problems pertaining to errors (240), inconsistencies (39), methodological issues (166) and information gaps (81) affect 154 of the 165 datasets totalizing 526 occurrences (Figure 1a), with more problem types per study in the freshwater realm (3.4 ± 1.5, Among the errors, 35 datasets considered taxa other than insects, arachnids or entognaths (hereafter collectively referred to as "insects" for brevity), most often including the entire invertebrate assemblage instead of insects only, sometimes biasing the trends to the point of reversal (Figure 2a). The composition of the invertebrate group selected from the source datasets was misreported in 55 datasets (e.g. Figure 2b). Insect numbers were misreported from source studies in 25 datasets because of calculation errors, inversion of numbers or species counted twice (e.g. Figures 2c and2d). Variation in sampling effort was not accounted for in 17 datasets (e.g. Figure 2e). Some datasets or plots had overlapping data for all or part of the time period ( 13datasets, e.g. Figure 2f). Finally, the geographic coordinates of the included plots were erroneous or not precise enough given the local scale objectives of the study in 88 datasets.

There were also a number of inconsistencies and information gaps. There were inconsistencies of taxa between plots of a same dataset (e.g. Figure 2e) or between metrics in a same plot ( 7datasets, e.g. Figure 2g); but users cannot identify them because the insect group is not indicated at the plot level. Unfounded inclusion, exclusion (e.g. Figure 2h) or pooling of original sites concerned 14 datasets. There were unfounded exclusions of data regarding a metric (abundance or biomass), some insect groups or some time records (6 datasets, Figure 2i). A total of 12 datasets had an inadequate temporal resolution (Appendix S1, Problems.xlsx). Moreover, dates were not indicated when there were successive samplings per period, which may lead users to consider samples as interchangeable replicates, when they are time-dependent (20 datasets).

Regarding methodological issues, the inclusion of studies with experimental conditions or major disturbances and the estimation of the local cropland cover are the focus of sections 3 and 4.

In addition, 22 datasets had only two years of data, while non-monotonic dynamics of insect populations call for more records. The number of sites and/or datasets was unduly inflated in 6 datasets compared to the original studies, leading to their overweighting.

Focus on the problematic inclusion of clams, snails, worms, shrimps in freshwater data

In the freshwater realm, 80% (19 out of 24) of the biomass and 40% (21 out of 54) of the abundance datasets included invertebrates other than insects. They were related to 28 distinct datasets, 24 of which involved the entire freshwater invertebrate assemblage (Figure 3, Appendix S2: Table S1).

The great majority of these 24 datasets included worms, mollusks and crustaceans, with notably Oligochaeta, Turbellaria and Amphipoda, often indicative of poor water quality (Appendix S2: Table S2). The inclusion of these datasets is not consistent with the purpose of the database, because the dynamics of insects cannot be inferred from those of invertebrate assemblages. This is illustrated by Figure 3a showing that insect and invertebrate assemblages can have contrasted trajectories (top), and that proliferating invasive mollusks can drive the trend of the invertebrate assemblage (bottom) (see also FreshwaterNonInsects.xlsx). The 62 freshwater datasets, 28 including non-insects (24 the entire assemblage). The insect share, extracted from 13 of these, averaged 48.5% with a ~35% CV. Photograph credits detailed in Appendix S2: Table S3.

Beyond these examples, we calculated that on average insects made up 48.5% ± 32.2% of the entire assemblage in the 13 datasets (48 plots) with information on all or part of the time records (Figure 3b, FreshwaterNonInsects.xlsx). The insect share in the assemblage was also highly variable over time (Appendix S2: Figure S1) with a coefficient of variation averaging 34.8% ± 22.5% in the 8 datasets (42 plots) where information was available for more than one time record.

Considering non-insects in the assemblage can therefore considerably alter the inferred 'insect' trend. Out of the 53 plots of 15 datasets with reported or inferred information on invertebrates driving the trend of the entire assemblage, non-insects (invasive mollusks, opportunistic oligochaetes and/or amphipods…) were found to drive the assemblage trend in half of the plots (25) and two-thirds of the datasets (10) (FreshwaterNonInsects.xlsx).

Inclusion of experimental or disturbed situations with internal drivers of insect change

A major limitation of the InsectChange database is that users are inclined to erroneously attribute insect changes to the ecological drivers extracted from external databases, when they more directly reflect habitat changes caused by -most often omitted-internal drivers investigated in the original studies. Indeed, 88 datasets were extracted from controlled or natural experiments or from strongly disturbed contexts. In addition, the factors originally investigated were not mentioned in 69% of these 88 datasets (Problems.xlsx). Among these 88 datasets, 14 controlled experiments tested the effect of one or several treatments in different plots (Figure 4a) and 53 natural experiments [START_REF] Diamond | Ecology: Laboratory, field and natural experiments[END_REF]) investigated the effect of a natural disturbance by comparing insect abundance in more or less disturbed plots (Figure 4b) or before and after the disturbance in a plot (Figure 4c). In these experimental datasets, control plots, experimental plots or both control and experimental plots were inconsistently included in InsectChange. In the remaining 21 observational studies, a strong disturbance affected insect trends (Figure 4d).

Among the 88 datasets, the studied factor could be negative, such as a severe drought, fire or pesticide application, creating deleterious then recovery conditions for insects at the beginning, middle or end of the observation period, this timing strongly influencing insect trends (Appendix S3). The factor could also be positive, such as cessation of harmful activities, remediation measures (Figure 4e), active restoration or creation of new habitats such as ponds (Figure 4f), reservoirs or nesting sites, favoring insect recovery or colonization. Insects were also expected to increase in 6 studies on polluted and eutrophicated freshwaters (Appendix S3) because stress-tolerant Of these 88 datasets affected by specific internal drivers, there were almost five times more situations expected to favor an increase rather than a decrease in insects (Figure 4g, Appendix S3). Two-thirds of the freshwater datasets were affected by internal drivers, a proportion significantly higher than that (one half) of the terrestrial datasets (χ² = 5.7, P=0.02, Figure 4h). Of these twothirds, insects were expected to increase in 61% of cases and decrease in only 15% of cases. We thus caution that InsectChange is unrepresentative of the habitat conditions and associated insect trends worldwide, particularly in freshwaters. In controlled experiments, it seems prudent to consider only control sites. Care should be taken regarding the representativeness of the situations investigated in natural experiments and of the overall studies in terms of sites without, under or post-disturbance [START_REF] Cardinale | Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends[END_REF].

Methodological issues resulting in a strong overestimation of the local cropland cover

Last, we found a strong overestimation of local cropland, a possible driver included in InsectChange by matching plots to land covers of the ESA CCI database (ESA 2017) via their geographic coordinates (GC). We found that the assessment of local cropland cover was inadequate for almost half (468 of 982) of the terrestrial plots, with a very uneven distribution of errors (Figure 5a, Appendix S4, CroplandCover.xlsx). Most plots assessed as having no surrounding crops were assessed (451 of 486), while most plots assessed as having surrounding crops suffered from an overestimation of the crop cover (350 of 496), two-thirds (233) having no surrounding crops.

We observed a similar picture when considering only plots with distinct GC (508) (Figure 5a).

Because of this strong bias, we argue that InsectChange cannot provide a reliable analysis of the impact of local cropland cover on insect change and could incorrectly dismiss the impact of crops on insect decline.

The main reason for these errors was an inaccurate assessment of the local cropland cover from the interpretation of satellite images in the ESA-CCI database (Appendix S4), notably due to its limitation that grasslands may inaccurately be coded as croplands (Desquilbet et al. 2020) and its imprecise representation of land cover when used at a local scale composed of nine 300 m × 300 m squares with a rough cropland cover assigned to each of them (Figure 5b). Second, the GC were inadequate for estimating land cover at this scale for 228 of the 982 plots, mainly (212 plots) because the considered plots aggregated data from sampling points often several kilometers apart under the unique GC of their barycenter or of one of these points (Figure 5b). Concerning freshwaters, the GC provided were inadequate for 108 of the 585 plots matched with ESA-CCI information and should be used with caution. We did not check local cropland cover estimates, as water quality at sampling points may be more dependent on upstream land use (Desquilbet et al. 2020). A quality check on the accuracy of estimates provided for other possible drivers of insect change is strongly recommended, notably local-scale drivers (urban cover and climate change) similarly affected by the inadequacy of GC.

Conclusion

The numerous problems underlying InsectChange call for corrections and an extreme vigilance in its use. They fully undermine the results obtained so far from this database, in the first place those of van Klink et al. (2020a), widely covered by the media, which cast unsubstantiated doubt on insect decline [START_REF] Kimbrough | Insects decline on land, far better in water, study finds. Mongabay, news & inspiration from nature's frontline[END_REF][START_REF] Mcgrath | Nature crisis: 'Insect apocalypse' more complicated than thought[END_REF]. We argue that InsectChange, in its current state, does not allow to study insect trends worldwide, to analyze the influence of agriculture on insects or to suggest a positive effect of water protection policies on freshwater insects. More generally, this careful review illustrates the importance of contacting dataset owners to ensure their appropriate use. It calls for vigilance to avoid transferring errors across databases, as occurred for 11 datasets incorporated from the Global Population Dynamics Database [START_REF] Prendergast | The Global Population Dynamics Database[END_REF] and/or Biotime (Dornelas et al. 2018) to InsectChange (Appendix S5). It underlines the need for relevant matching with external databases. Finally, our analysis highlights the attention that needs to be paid to the data and its meaning to ensure that large databases built from individual datasets participate in a cumulative knowledge process.

FIGURE 1

 1 FIGURE 1 Problems encountered in the InsectChange database (Problems.xlsx). (a) The 17 types of problems and numbers of datasets involved. *Cropland cover assessment only for terrestrial studies. (b) Number of problem types per freshwater and terrestrial dataset (except those related to cropland cover assessed only for terrestrial studies) and associated means (± SE).

FIGURE 2

 2 FIGURE 2 Errors and inconsistencies in data selection biasing insect change assessment (Appendix S1, Problems.xlsx, Fig2and4.xlsx). Examples of errors (a-e), inconsistencies regarding taxa over plots or metrics (f, g), inconsistencies regarding data selection (h, i).

FIGURE 3

 3 FIGURE 3 Freshwater time-series including non-insects while the insect share was often low and variable over time (Appendix S2, FreshwaterNonInsects.xlsx). (a) Case studies 1455 and 1466 (Appendix S1) illustrating (top) contrasted trajectories of the entire assemblage and insects only, and (bottom) invasive non-insects driving the trend. (b)The 62 freshwater datasets, 28 including non-insects (24 the entire assemblage). The insect share, extracted from 13 of these, averaged 48.5% with a ~35% CV. Photograph credits detailed in Appendix S2: TableS3.

FIGURE 4

 4 FIGURE 4 Inclusion of studies with specific internal drivers (Appendix S3, Fig2and4.xlsx). Examples of (a-c) controlled or natural experiments and (d-f) studies with major disturbances; in (f), dashed and purple curves respectively represent erroneous and actual data. (g) Insect trends expected under the internal drivers with (h) the most problematic case of freshwaters.

FIGURE 5

 5 FIGURE 5 Overestimation of local cropland cover (Appendix S4, CroplandCover.xlsx). (a) Assessment errors of plot cropland cover and their unevenness. (b) Example of overestimation of the local cropland cover (Study 1102, Plot 567) outlined in red; 1) InsectChange assessment: cropland cover = (8×100%)/9 = 89% based on ESA-CCI; 2) our assessment: cropland cover ≤ 2% based on Google Earth and information from the source study.

Acknowledgments

This work was publicly funded by the French National Research Agency (ANR-17-EURE-0010 to MD, ANR-16-IDEX-0006 to LG, Investissements d'avenir program). We thank Sonja Jähnig for helpful discussions, Gaëlle Viennois for double-checking the land cover analysis, Dirk Maes and Hans van Dyck for their help in analyzing biased data on Belgian lepidopterans (study 70), other co-authors of Desquilbet et al. (2020), and Marianne Elias, Marilyne Laurans, Doyle McKey and Denis Bourguet for helpful comments on earlier drafts.

Author contributions

Desquilbet and L. Gaume contributed equally to this work.

Conflict of interest statement

The authors declare no conflict of interest.

Supplementary information

Five appendices (Appendix S1, Appendix S2, Appendix S3, Appendix, S4 and Appendix S5) and four datasheets (Problems.xlsx,FreshwaterNonInsects.xlsx,CroplandCover.xlsx and Fig2and4.xlsx) are available in the supplementary materials.

-Appendix S1 and Problems.xlsx describe and record the problems encountered in InsectChange.

-Appendix S2 summarizes the inclusions of freshwater non-insects in the InsectChange assemblages, while FreshwaterNonInsects.xlsx details the calculation of their parts in the assemblages and the problematic consequences on the inferred 'insect' trends.

Appendix S3 shows the expected trends in the studies of InsectChange affected by internal drivers.

-Appendix S4 shows the assessment of the adequacy of geographic coordinates and local cropland cover, which are detailed in CroplandCover.xlsx.

-Appendix S5 details the errors transferred from other databases to InsectChange.

-Fig2and4.xlsx provides information supporting Figures 2 and 4 of this comment.