Yang Zhou

Stéphane Maisonneuve

Lorenzo Casimiro

Pascal Retailleau

Juan Xie

François Maurel

Rémi Métivier

Photoisomerization of a 4-dicyanomethylene-2-methyl-6-( p -dimethylaminostyryl)-4 H -pyran analog dye: a combined photophysical and theoretical investigation

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Introduction

Molecular photoswitchable systems consist of chromophores that can be interconverted by light stimuli between at least two states, resulting in different photochemical and photophysical properties. Light-induced reactions involve photoinduced electrocyclization, cycloreversion, dimerization, photoinduced bond cleavage, E↔Z or cis↔trans isomerization. Among these mechanisms, the double-bond E↔Z photoisomerization is prevailing in light-driven molecular switches, rotors and sensors due to its flexibility and embeddability. [1][2][START_REF] Roke | Proc. Nat. acad. Sci[END_REF][START_REF] Kornman | [END_REF] The tunable optical and mechanical properties upon irradiation provided by the double-bond isomerization allow applications such as light-sensitive sensors and functional materials. [5][6][7] The N=N, C=N and C=C bonds are the most common photoswitchable groups in azobenzenes 8,9 , acylhydrazones [10][11][12] , and stilbenes 13 , respectively. Besides, 4-dicyanomethylene-2-methyl-6-(p-dimethylaminostyryl)-4H-pyran (DCM) analogs are well-known red-emitting dyes, used in lasers, [15][16][17] in capping layers of organic light emitting diodes (OLEDs) or in dye-sensitized solar cells (DSSCs). [18][19][20][21][22] Its donor-π-acceptor structure confers it a noticeable intermolecular charge transfer character and consequently, a strong solvatochromism. Because of its central C=C double-bond, DCM can undergo an E↔Z isomerization under irradiation. However, the photoisomerization properties of DCM have been ignored in the vast majority of reports, with a few notable exceptions. 14,[23][24][25][26][27][28][29] The E↔Z photoisomerization studies and the related conversion yield quantification of a DCM analog (DCM-o-alkyne), have been reported by our group. 14 In this previous article, we described the steady-state spectroscopy, HPLC analysis, conversion yield and photostationary states (PSSs), characterization of the E-form and Z-form, under illumination, at different wavelengths and in different solvents.

Indeed, the E-forms of DCM-o-alkyne were found to be fluorescent, and not the Z-forms. Thus, the fluorescence modulation associated to the photoisomerization demonstrates the high potential of DCM analogs as intrinsic photoswitchable fluorophores. The previous studies on DCM, using spectroscopic or quantum chemical calculations, indicated the existence of 4 stable isomers (E-and Z-forms) and conformers (s-cis-and s-trans-forms) and their possible interconversions. In the present paper, we focus our attention on the DCMazide 30 , an azido variant of the previously studied DCM-o-alkyne, allowing further functionalization. The molecular structures of the four isomers/conformers of DCM-azide are shown in Figure 1. Like other molecular systems with asymmetric stilbene core, the detailed description of the behavior of DCM-azide under illumination in its different isomers/conformers is not straightforward, and the interconversion modes between the four geometries are still not clearly understood. In this context, the combination of spectroscopic and computational approaches can be helpful to rationalize the DCM-azide properties. We report here the experimental and theoretical studies of DCM-azide involving its four forms, to bring in-depth description of its photophysical and photochemical properties. X-ray structures, steady-state and time-resolved spectroscopy data, as well as quantum chemical computations in the ground state, the excited state, or at conical intersections, provide important information on the photoisomerization pattern of DCM-azide.

Results and discussion

Structural studies

The DCM-azide has been synthesized as pure E-form according to the previously reported procedures 30 by shielding the reaction and post-procedures from ambient UV-visible light exposures. By the different eluent conditions on the column chromatographies, the DCM-azide can be obtained as a red crystalline powder. In the following, we denote two key dihedral angles, O 3 -C 4 -C 9 = C 13 and C 4 -C 9 = C 13 -C 14 of the molecular layout in Figure 1, as D1 and D2, respectively.

After recrystallizing the crystalline powder, two different polymorphs were obtained as single crystals. The corresponding structures were successfully solved by X-ray diffraction (XRD) analyses, as shown in Figure 2 and in the ESI (Figure S1 and Table S1): the first one crystallizes in the triclinic P-1 space group, whereas the second one adopts an orthorhombic Pnma space group. The molecular packing mainly influences the orientation of the azido substituent, while the whole structure is in both cases thoroughly planar across the central double-bond of DCM-azide, shown in the s-trans-(E) configuration, for both structures (see ESI, Figure S2). The two polymorphs differ mainly in the intermolecular contacts, as shown by Hirshfeld surface analyses described in ESI (Figures S3-S5). The molecular geometry of DCM-azide is defined by the two main dihedral angles D1 and D2 (D1 = 4.7°and D2 = 179.9°f or the triclinic form, and D1 = 0.0°and D2 = 180.0°for the orthorhombic form), and the corresponding C 4 -C 9 single-bond (length = 1.440 Å and 1.434 Å for triclinic and orthorhombic forms, respectively) and C 9 = C 13 doube-bond (length = 1.335 Å and 1.340 Å for triclinic and orthorhombic forms, respectively). These geometrical parameters are fully in line with the previously described DCM-o-alkyne derivative. In the previous study on DCM-o-alkyne, the THF solvent (medium polarity) was revealed to be an ideal trade-off between the fluorescence quantum yield and the E→Z photoisomerization conversion yield, outperforming acetonitrile and toluene in terms of fluorescence photoswitching benchmark. Therefore, we decided to employ THF as the main solvent in the present study of DCM-azide. In the steady-state UV-visible measurements, DCMazide shows an absorption λ max at 459 nm in THFm, with a molar absorption coefficient recorded at ε 459 nm = 48700 L mol -1 cm -1 . After reaching a photostationary state under irradiation at 485 nm (PSS485), the λ max was shortened and the absorption bands around 350 -450 nm were flattened. Furthermore, from the PSS485 solution, we extracted the Z-form of DCM-azide by thin layer chromatography (TLC), and measured its absorption spectrum in THF. The corresponding dotted absorption spectrum shown in Figure 1 presents a much weaker and blue-shifted band at 447 nm, and the absorption is stronger in the 300-400 nm range than the synthesized E-form.

The fluorescence spectrum of DCM-azide in the E-form is broad and intense around 590 nm, and its emission quantum yield is appreciable (Φ f = 0.25) but lower than that of DCM-o-alkyne (Φ f = 0.32), 14 due to its azido lateral part. Fluorescence decay curves of DCM-azide were recorded at different excitation wavelengths by the time-correlated single-photon counting (TCSPC) technique (Figure 3). The fluorescence decays were fitted by a global analysis procedure using a bi-exponential function, which was necessary to obtain acceptable curve fitting (χ 2 R < 1.2). Two decaytimes were identified: the first decay-component (τ 1 ) is found at 0.84 ns contributing to the major part of the fluorescence (large pre-exponential factor α 1 > 0.88 and fraction of intensity f 1 > 0.97), whereas the second decay-time is shorter (τ 2 = 0.20 ns) and has a minor contribution to the fluorescence intensity (α 2 < 0.12 and f 2 < 0.03). We collected the fluorescence decays at different excitation wavelengths from 315 nm to 450 nm and irradiated the DCM-azide sample at 485 nm to make cross comparisons, and further evaluate the possible contributions of the Z-isomer to the emission. As a result, the change of excitation wavelength or irradiation did not influence the collected fluorescence decays: τ 1 and τ 2 were found to be identical, and α 1 = 0.91 ± 0.03, α 2 = 0.09 ± 0.03 were similar for all the measurements, as shown in Figure 3 and Table 1. Therefore, the two decay-time components are not related to the E↔Z photoisomerization process. The results from Table 1 fully confirm the interpretation reported in our previous paper. 14 From fluorescence decays collected at different excitation wavelengths, with or without irradiation, we can conclude that the Z-forms of DCM-azide are completely non-emissive, and the fluorescence components (τ 1 and τ 2 ) identified by fluorescence decays correspond to s-trans-(E) and s-cis-(E) conformers.

Absorption and emission electronic transitions of s-trans-(E) isomer

From the X-ray structure of DCM-azide, we assume that the strans-(E) isomer corresponds to its most stable isomer. Moreover, the steady-state and time-resolved fluorescence results demonstrated that the s-trans-(E) isomer is considered to represent the main contribution to the fluorescence. Therefore, absorption and emission energy calculations at the level of density functional theory (DFT) and time-dependent density functional theory (TDDFT) were first performed for this isomer. Computations with polarizable continuum model (PCM) for THF, three functionals (PBE0, CAM-B3LYP, ωB97X-D), and the 6-311+G(d,p) Pople basis set were performed for DCM-azide in the s-trans-(E) form. All electronic transition energies were calculated by the linearresponse method. Other non-equilibrium solvation calculations were also tested, as reported in the ESI (corrected linear-response and state-specific, see Table S6). By screening the DFT functionals, PBE0 shows the most accurate estimation of the DCM-azide absorption (λ abs = 463 nm, compared to the experimental value λ max = 459 nm) and at the same time rather acceptable emission estimation compared to the experiment (λ em = 521 nm vs. experimental λ max = 576 nm). Despite the donor-acceptor character of DCM-azide, ωB97X-D and CAM-B3LYP did not provide better absorption or emission energies compared to PBE0, with systematic overestimation of transition energies and shorter maximum wavelengths than those measured experimentally. Only the Stokes shift is more accurately described with the long-range corrected CAM-B3LYP. Considering all the functional screenings and computational costs, we decided to select PBE0 as the main functional for the DFT studies, for more intuitive comparisons and uniformity over the whole theoretical investigation (see "Excited state computations" paragraph below).

We analyzed the frontier molecular orbitals of the four isomers/conformers of DCM-azide at PBE0/6-311G++(d,p) level with PCM model of THF solvent, as reported in the ESI (Figure S6). For all forms, the highest occupied molecular orbitals (HOMOs) and the lowest unoccupied molecular orbitals (LUMOs) are the main orbitals involved in the first electronic transition S 0 → S 1 . The HOMOs (respectively LUMOs) have a clear π (respectively π * ) character. Therefore, the S 0 → S 1 transition has a π → π * nature, with a limited charge-transfer character due to a small electronic delocalization from the methylamino-styryl moiety to the dicyano-pyran moiety. Multiple contributions of the HOMO-1, HOMO, LUMO and LUMO+1 to the S 0 → S 2 and S 0 → S 3 electronic transitions are noted. In a similar manner, such features were also identified for the other functionals tested, namely ωB97X-D and CAM-B3LYP, and other polar solvents (see ESI, Figures S7-S10). The ground-state pathway of the interconversion between the s-trans and s-cis conformers can be followed by rotating the C 4 -C 9 single-bond, thus changing the D1 dihedral angle, the E-isomer being transformed into its corresponding Z-isomer by changing the D2 dihedral angle (rotation around the C 9 = C 13 double bond). The scanning plot of these energy minimized paths along the two rotating dihedral angles D1 and D2 is presented in Figure 4. For the s-cis↔s-trans interconversion, the energy barriers are calculated to be 0.41 eV and 0.16 eV for the E-side and the Z-side, respectively. Regarding the E↔Z reaction, the curves show that the closed-shell formalism cannot be used for double-bond rotation but clearly illustrate that the E↔Z barrier of isomerization is much higher than the s-cis↔s-trans process, as expected. Therefore, the ground-state s-cis↔s-trans transformation can occur spontaneously at room temperature, whereas the E↔Z requires external energy input to take place, such as photon absorption. The C 4 -C 9 and C 9 = C 13 bond lengths vs. scan coordinates D1 and D2 are shown in the ESI (Figure S11, blue and red lines, respectively), presented by the blue and red lines, respectively. Along the s-cis↔s-trans interconversion, the double bond remains almost unaffected, whereas the single bond is elongated to a maximum at the transition states, then shortened at the four stable isomers and conformers structures. Along the E↔Z isomerization paths, the double bond is elongated and the single bond is shortened to reach comparable bond lengths at the transition states (C 9 = C 13 max. = 1.419 Å, C 4 -C 9 min. = 1.399 Å). When the molecule is distorted around D2 = 75°, the C 4 -C 9 = C 13 -C 14 loses the alternate single-double-single bond character at this peculiar geometry. Indeed, the E↔Z transformation is a configura-tional isomerization, which requires that the double bond adopts a "single bond" character. In the present case, these elements lead us to assume that the E↔Z photosiomerization is allowed in the excited state (see below).

Ground-state potential energy surface

As previously described, the four stable forms of DCM derivatives are denoted as s-trans-(E), s-cis-(E), s-trans-(Z), s-cis-(Z)

Excited-state computations

The role of the dihedral angle D1 corresponding to the s-cis↔strans interconversion processes, was first investigated in the excited state. We performed the relaxed scan optimization of their lowest excited-state S 1 PECs with a downsized def2-SVP basis set, as well as the S 1 vertical excitation energies (VEEs), as shown in the ESI (Figure S12). By examining the s-cis↔s-trans excitedstate interconversion, the optimized S 1 energy levels are only slightly lower than the S 1 VEEs i.e., the lowest excited-state geometries are almost identical to the Frank-Condon ones. The scan optimizations of the S 1 states imply even higher energy barriers than the ground states for the two conformational interconversions: 0.70 eV in the S 1 vs. 0.44 eV in the S 0 (s-cis-(E)→s-trans-(E)) and 0.40 eV in the S 1 vs. 0.17 eV in the S 0 (s-cis-(Z)→strans-(Z)). On the other side, regarding the E↔Z isomerization, TDDFT optimizations could not be performed due to the possible conical intersection (CI) encountered, which requires other multi-determinantal methods.

As an extension to explore the potential energy surface (PES) along the E↔Z isomerization, we searched the possible existence of CIs on the reaction path by using the Spin-Flip TDDFT (SF-TDDFT) method. 31,32 SF-TDDFT is a single reference method, easy to carry out to compensate the inability of TDDFT to localize S 0 /S 1 CIs. 33,34 Taking a higher triplet state with two unpaired α-electrons as the reference state instead of the ground state used in TDDFT, SF-TDDFT is able to describe doubly-excited electron configurations via α → β spin-flipped excitations necessary to calculate CIs where the Born-Oppenheimer approximation breaks down. However, we need to pay attention to the spin contamination problem brought by the single reference method. When applied to double-bond molecular systems, e.g. stilbene or azobenzene, the SF-TDDFT method with different algorithms has successfully yielded results close to the CASSCF/CASPT2 level of calculations at a rather lower computational cost. [33][34][35] In the present case, we adapted SF-TDDFT with the penalty constrained algorithm to DCM-azide, which is a 54-atom dimension system, searching the minimum energy CIs on the S 0 /S 1 PES. 33 To localize the CI, we firstly implemented a S 0 PES scan map with a reduced basis set size at 6-31G(d) in vacuo. The global S 0 PES is shown in Figure 5. Despite slight energy differences, the results are well-compatible with the larger basis set used with the PCM solvation model (Figure 3). Then we assumed the CIs to be located close to the maximum energy barrier between the Eand Z-forms. We defined a set of initial geometries corresponding to the S 0 PES map, with D2 = -75°and D1 ranging from -180°and +180°, sampling one structure every 15°. With this methodology, two CI(S 0 /S 1 ) geometries were successfully identified as depicted in Figure 5 and detailed in ESI (Tables S7-S8), of the s-cis type (CI-1: D1 = 176.66°, D2 = -78.58°) and s-trans type (CI-2: D1 = 18.88°, D2 = -87.40°), respectively. At the first glance, the CIs In addition, it is interesting to note that the electronic nature of the S 1 state at the crossing could be provided by the SF-TDDFT and was found to have an ionic HOMO→LUMO character. Moreover, we computed the S 1 minima for the s-trans-(E), s-cis-(E), s-trans-(Z), s-cis-(Z) forms, at the SF-PBE0/6-31G(d) level in vacuum, to be compared with CI-1 and CI-2. Their relative energies and dihedral angles (D1 and D2), together with those of CI-1 and CI-2, are gathered in Table 3. The energy difference between the optimized S 1 state and the CI is higher for the s-trans-(E) form (∆E = 0.51 eV). The lowest energy differences are obtained for the Z-forms (∆E = 0.22-0.42 eV). The s-cis-(E) is located at an intermediate level (∆E = 0.45 eV). Additionally, the optimized S 1 states for both E-forms show a fully planar geometry, whereas they appear more tilted for the Z-forms, with 5-9°away from planarity for D1, and 18-24°for D2. The geometries of the excited state of the Z-forms being closer to the CI ones, compared to the planar E-forms, we can therefore expect a much lower energy barrier in the excited state landscape to reach the CI after excitation and return non-radiatively to the ground state, possibly along with photoisomerization. Consequently, we can deduce that the DCM-azide molecule has several pathways to relax from the excited states to the ground state. When DCM-azide is excited, it can undergo an isomerization in the excited state, through the CI to reach the ground state, which is fully compatible with the experimental evidence of the E↔Z photoisomerization. Besides, the relaxed excited states of DCM-azide can return to the ground state by radiative deactivation. Since the S 1 excited state of the s-trans-(E) form has a larger energy difference than the s-cis-(E) form to reach the CI, it could explain its much larger contribution to the overall emission and its longer lifetime. The S 1 excited states of the Z-forms being much closer to the CI (in geometry and energy), it is fully in line with the absence of observed fluorescence from these species, as well as the higher photoisomerization quantum yield measured previously for the Z→E reaction, compared to the E→Z reaction. 14 As a unified general picture, after light absorption, the E-forms may experience a competition between fluorescence deactivation, E→Z isomerization reaction (through CIs), and other nonradiative deactivation pathways. Regarding the Z-forms, which are not emissive, only the Z↔E isomerization reaction (through CIs) and other non-radiative deactivation paths can be considered. It is worth noting that DCM is expected to have both locally excited and internal charge-transfer states in solution, at the origin of its well-known solvatochromism in emission. In the present CIs determination in vacuo, the solvent could not be taken into account and consequently, the role of the internal charge transfer state could not be properly modeled. The solvent effect is beyond the scope of our computational investigation.

Conclusions

The photoisomerization of a DCM analog (DCM-azide) was investigated experimentally and theoretically, shining light on its E↔Z isomerization, which was demonstrated previously. 14 The relationships between the four isomers and conformers of DCMazide were clarified by both experimental evidences (X-ray structures and spectroscopic data) as well as a full set of theoretical calculations (geometries, energies, electronic transitions, isomerization and interconversion pattern) in vacuo or including the solvent medium.

The fluorescence decay experiments confirmed that only the E-forms of DCM-azide are emissive. The emissive species in the fluorescence decay experiments were assigned to the s-trans-(E) (longer decay-time) and s-cis-(E) (shorter decay-time) forms of DCM-azide. The DFT/TDDFT and SF-TDDFT calculations of the four isomers and conformers provided a general pattern of the isomerization and interconversion paths in the ground and the excited states. Two conical intersections were identified, explaining the relaxation from the lowest excited state to the ground state, at the origin of the E↔Z photoisomerization. They obviously play a great role in the competition between the fluorescence relaxation and the E↔Z photoreaction, but also the s-trans↔s-cis conformational interconversion.

Experimental section and methods

Materials

All solvents used for steady-state spectroscopies were performed in spectroscopic pure grade reagents from Carlo Erba Reagents. The DCM-azide compound was prepared according to literature procedures. 30,36,37

Steady-state absorption and emission spectroscopy

Steady-state spectroscopic absorption measurements were carried out with Agilent Technologies Cary 4000 and Cary 5000 spectrophotometers. The emission spectra were recorded with a Horiba Jobin-Yvon Fluorolog FL3-221 fluorescence spectrometer. The fluorescence quantum yield was determined by using Coumarin 153 in ethanol (φ em = 0.544 38 ) as a standard reference from Sigma-Aldrich.

Time-resolved fluorescence spectroscopy

Fluorescence decay curves were obtained by the time-correlated single-photon counting (TCSPC) method. The setup is composed of a titanium sapphire Ti:Sa oscillator (Spectra Physics, Maï Taï) emitting pulses of 100 fs duration for the fundamental laser beam, at a 80MHz frequency. The laser pulses then pass through a pulse picker which implements an acousto-optic modulator to reduce the repetition rate down to 4MHz. Non-linear SHG/THG crystals generate the desired wavelength (GWU Lasertechnik, UHG-23-PSK), then the beam is directed to the sample solution after adjusting the excitation power with an intensity attenuator filter wheel. Fluorescence photons are detected at 90°through a polarizer at the magic angle and a monochromator, by means of a micro-channels plate photomultiplier (MCP-PMT R3809U-50, Hamamatsu), connected to a TCSPC module (SPC-630, Becker & Hickl). Time-correlated fluorescence decay data are finally processed and analysed with the help of a software which implements reconvolution analysis and the global nonlinear lastsquares minimization method (Globals, Laboratory for Fluorescence Dynamics at the University of California, Irvine).

X-ray crystallographic analysis

X-ray crystallographic analysis was carried out on the DCMazide crystals. The isolated crystals were dried under vacuum at room temperature and shielded from the ambient light. X-ray diffraction was carried out using Rigaku XtaLabPro diffractometer equipped with a Mo microfocus sealed tube generator coupled to a double-bounce Max-Flux multilayer optic and a HPAD PI-LATUS3 R 200K detector. Data collections were carried out using the software CrysAlisPro 1.171.41.96a. [START_REF] Rigaku | Rigaku Oxford Diffraction[END_REF] SCALE3 ABSPACK scaling algorithm implemented within CrysAlisPro was applied to the processed data for the empirical absorption correction using spherical harmonics. The two structures were readily solved by intrinsic phasing methods (SHELXT program) [START_REF] Sheldrick | [END_REF] and refined by full-matrix least-squares methods on F 2 using SHELX-L. 41 Molecular graphics were prepared with ORTEP. [START_REF] Burnett | ORTEP-III[END_REF]

Computational details

All the DFT and TDDFT calculations were performed by the Gaussian 16 software package. [START_REF] Frisch | Gaussian 16 Revision C.01[END_REF] The Pople 6-311++G(d,p), 6-311+G(d,p) and 6-31G(d) basis sets were adopted and the solvent effects were implemented with integral equation formalism of the polarizable continuum model (IEFPCM). The S 0 geometries of the four isomers and conformers were optimized at the PBE0/6-311++G(d,p) level. Regarding the computational cost, reduced size def2-SVP and 6-31G(d) basis sets were used for the excited states explorations and corresponding comparisons. The comparison of functionals and the basis set dependencies are shown in the ESI. From all optimized geometries, the vertical excitation energies and emission energies were calculated by the TDDFT method. The linear response (LR) method was implemented in the TDDFT calculations. The minimum energy CI calculations of S 0 /S 1 in the E↔Z isomerization process were located by the SF-TDDFT with the penalty-constrained (PC) optimization algorithm. 33,34,[START_REF] Levine | [END_REF]45 The SF-TDDFT/PC CI localizations were car-ried out at the PBE0/6-31G(d) level in gas phase by GAMESS-US (version 30 SEP 2020 (R2)). 46 All visualizations were implemented by GaussView 6 and VMD 1.9.3. [START_REF] Dennington | GaussView Version 6[END_REF][START_REF] Humphrey | [END_REF]

a

  Université Paris-Saclay, ENS Paris-Saclay, CNRS, Photophysique et Photochimie Supramoléculaires et Macromoléculaires, 91190 Gif-sur-Yvette, France. E-mail: remi.metivier@ens-paris-saclay.fr b Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, UPR 2301, Gif-sur-Yvette, France. c Université de Paris, ITODYS, CNRS, F-75006 Paris, France. E-mail: maurel@univparis-diderot.fr † Electronic Supplementary Information (ESI) available: X-ray diffraction analyses, details of DFT/TDDFT and conical intersections calculations. See DOI: 10.1039/cXCP00000x/

Fig. 1 (

 1 Fig. 1 (a) Molecular structures of DCM-azide in its different isomers and conformers, noted s-trans-(E), s-cis-(E), s-trans-(Z ), s-cis-(Z ) according to our previous study 14 . Dihedral (O 3 -C 4 -C 9 = C 13 ) involving C-C single bond is defined as D1 and dihedral (C 4 -C 9 = C 13 -C 14 ) involving C=C double bond is defined as D2. (b) Absorption spectra of the E-isomer (full line on the left) and the Z-isomer (dotted line on the left) together with E-isomer emission (dashed line on the right) are recorded in THF solution.

14

 14

Fig. 2 X

 2 Fig. 2 X-ray diffraction structures in the triclinic form (left) and in the orthorhombic form (right) of the s-trans-(E) form of DCM-azide obtained after crystallization.

Fig. 3

 3 Fig. 3 Fluorescence decay curves, instrumental response function, biexponential fitting and weighted residuals of DCM-azide at different excitation wavelengths (315 nm, 350 nm, 400 nm and 450 nm) in THF. The marked decay (*) corresponds to a DCM-azide THF solution irradiated at 485 nm.

Fig. 4

 4 Fig. 4 The S 0 Potential energy curves of the DCM-azide isomers/conformers interconversion by rotating the main dihedral angles D1 and D2, D1 corresponding to the O 3 -C 4 -C 9 = C 13 scan coordinate and D2 corresponding to the C 4 -C 9 = C 13 -C 14 scan coordinate (See Figure 1). DFT calculations were performed at the PBE0/6-311+G(d,p) PCM:THF level of theory. The calculated scans were performed along the following coordinate directions: s-cis-(E)→s-cis-(Z ), s-cis-(E)→s-trans-(E), s-trans-(E)→s-trans-(Z ) and s-cis-(Z )→s-trans-(Z ). The s-cis side is shown in red color and the s-trans side is shown in blue color. NB: the final states reached in the end of scans may slightly differ from the fully optimized forms (as visible e.g. for s-cis-(Z ) and s-trans-(Z ))

  Their corresponding ground state geometries and energies were optimized at the DFT PBE0/6-311G++(d,p) level (ESI, Tables S2-S5). s-trans-(E) shows the lowest energy, which confirms the X-ray structure analysis. Both E-forms have straight planar geometries across the double-bond moiety. Within a PCM model of THF solvent, the dipole moment of s-trans-(E) records the highest value at 20.0 D, while s-cis-(Z) has the lowest dipole moment 12.8 D amongst the four (ESI, Figures S6).

  The dipole moments were enhanced in more polar solvents such as acetonitrile and DMSO (see ESI, FiguresS9-S10) with more stabilized energies.

  are close to the location of the highest energy barrier structures of the S 0 PES. The s-cis CI-1 is geometrically close to the ground state geometry taken at the transition state between s-cis-(E) and s-cis-(Z) as shown in Figure 5. The CI-2 geometry found on the s-trans side is more displaced from the saddle point of the ground state between the s-trans-(E) and s-trans-(Z). However, the two CIs identified by SF-TDDFT have a similar S 0 /S 1 energy at this calculation level (CI-1: S 0 /S 1 = -1292.8950/-1292.8935 a.u. and CI-2: S 0 /S 1 = -1292.8945/-1292.8935 a.u., respectively), with comparable single C 4 -C 9 and double C 9 = C 13 bond lengths (CI-1: 1.399/1.464 Å and CI-2: 1.395/1.456 Å, respectively). Both CI-1 and CI-2 most probably originate from the same seam of intersection between S 0 and S 1 , along the D1 dihedral angle rotation.

Fig. 5

 5 Fig. 5 Ground-state potential energy surface (PES) calculated by DFT (PBE0/6-31G(d)) in vacuo as a function of D1 and D2 dihedral angles (color map), and minimum energy conical intersection (CI) geometries localized in the S 0 PES map, calculated by the SF-TDDFT method at the PBE0/6-31G(d) in vacuo level of theory.

Table 1

 1 Time-resolved fluorescence parameters of DCM-azide in the Eform, determined in THF.

	315	0.84	0.89(0.97) 0.20	0.11(0.03)
	350	0.84	0.93(0.98) 0.20	0.07(0.02)
	400	0.84	0.88(0.97) 0.20	0.12(0.03)
	400*	0.84	0.89(0.97) 0.20	0.11(0.03)
	450	0.86	0.92(0.98) 0.17	0.08(0.02)

λex(nm)

τ 1 (ns) α 1 ( f 1 ) τ 2 (ns) α 2 ( f 2 ) *: after irradiation to at 485 nm (PSS485)

Table 2

 2 Absorption and emission transition energies of s-trans-(E) form DCM-azide, calculated by TDDFT with the 6-311G++(d,p) basis set in THF, together with experimental data recorded for the E-form in THF solution.

		PBE0	CAM-B3LYP	ωB97X-D	Exp. data
		(eV) (nm) (eV) (nm) (eV) (nm) (eV) (nm)
	λ abs	2.68 463	3.06 405	3.15 394	2.70 459
	λ em	2.38 521	2.44 509	2.45 507	2.15 576
	Stokes shift	2404 cm -1	5045 cm -1	5657 cm -1	4425 cm -1

Table 3

 3 Energies and dihedral angles of optimized S 1 and CI, computed at the SF-PBE0/6-31G(d) level.

		∆E of S 1 (eV)	D1 (°)	D2 (°)
	S 1 of s-trans-(E) S 1 of s-cis-(E) S 1 of s-trans-(Z) S 1 of s-cis-(Z)	-0.058 0.290 0.088	0.12 -179.32 4.66 -171.35	179.41 179.74 18.63 23.92
	CI-1 (s-cis)	0.512	171.66	-78.58
	CI-2 (s-trans)	0.511	18.88	-87.40

1-8 J o u r n a l N a me , [ y e a r ] , [ v o l . ] ,

J o u r n a l N a me , [ y e a r ] , [ v o l . ] ,

Acknowledgements

The authors acknowledge Arnaud Brosseau for his kind help and support during the time-resolved fluorescence measurements. This work was performed by using HPC resources from the "Mésocentre" computing center of Centrale-Supélec and the École Normale Supérieure Paris-Saclay supported by the CNRS and the Région Ile-de-France (http://mesocentre. centralesupelec.fr/). Funding from the Agence Nationale de la Recherche (ANR-17-CE07-0056-01) is acknowledged.

Notes and references

Conflicts of interest

There are no conflicts to declare.

Author contributions

Conceptualization


Alexandre Naprous

Marta Avalos-Fernandez

Catherine Pradeau

Emmanuel Lagarde

Cédric Gil-Jardiné

Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports

By focusing on symptoms and not diagnoses, the socalled syndromic surveillance system gains in immediacy what it loses in specificity with respect to other more traditional options for public health surveillance. Reports of calls to emergency medical communication centers (EMCC) supplemented by the data collected by the rescue workers who arrived on the scene constitute a cost-effective and rich source of information. Unfortunately, EMCC data are infrequently used and their utility has not been demonstrated. The aim of this study was to explore the usefulness for public health surveillance of EMCC data when analyzed using text mining and visualization tools. Transformerbased deep learning architectures were used to classify call reports according to the reason for the call. We also extracted indicators that could serve as proxy measures using a keyword-search algorithm. We then developed a dashboard visualization tool to enable dynamic and spatial exploratory analyses. Finally, we explored the potential of this tool for two applications. While the tool proved unable to detect Covid-19 outbreaks, it appeared to be promising for a better understanding of territorial inequalities in healthcare access.

Introduction

Syndromic surveillance is an approach in which automatic data recording procedures allow the provision of data based on signs, symptoms or preliminary diagnoses for near-realtime tracking unexpected health events, monitoring expected trends, and conducting health impact assessment of infectious or environmental hazards. Syndromic surveillance can supplement traditional Public Health surveillance by analyzing less specific data from clinical and non-clinical sources. Non-clinical sources include social Medias, web searches, telephone helplines, mobile phone applications, pharmacy sales, etc. Clinical sources include data collected from calls to emergency medical communication centers (EMCC) and hospital emergency room (ER) data. While ER data are widely used for syndromic surveillance, data collected from the other emergency medical services are less frequent.

Yet, the data recorded during calls to EMCC possibly enhanced with data transmitted by the ambulance person-Copyright © 2022by the authors. All rights reserved. nel when the dispatch of an ambulance is necessary, constitute a cost-effective and rich source of information including temporal, spatial and standardized/unstandardized clinical data. These data have the potential of providing help in early detection of health events in the general population or subpopulations [START_REF] Duijster | Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review[END_REF]). This could cover information about less severe health conditions, not requiring emergency transfers and uncovered by ER data but that could be useful for Public Health surveillance. However, because the primary goal of the EMCC is not diagnostic accuracy but resource allocation, the specificity of the data may be low. The few studies that have used EMCC data have mainly focused on the surveillance of infectious diseases [START_REF] Duijster | Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review[END_REF], thought there are also examples of studies on alcohol and drug intoxication [START_REF] Holzer | Ten-year trends in intoxications and requests for emergency ambulance service[END_REF][START_REF] Manca | Estimating the burden of alcohol on ambulance callouts through development and validation of an algorithm using electronic patient records[END_REF], mental health [START_REF] Duncan | Epidemiology of emergency ambulance service calls related to men-tal health problems and self harm: a national record linkage study[END_REF], stroke [START_REF] Seo | Barriers and disparities in emergency medical services 911 calls for stroke symptoms in the United States adult population: 2009 BRFSS survey[END_REF]) and trends [START_REF] Lo | Overview of the shenzhen emergency medical service call pattern[END_REF]Gil-Jardiné et al. 2021b). Its utility in Public Health surveillance has not been fully demonstrated.

In France, EMCC, the so-called SAMU-Center 15 (emergency medical call center -phone number 15), are free public call centers, which operate 24/7 and respond to all health emergency calls for a defined area. They are the French equivalent of 911 in the USA and 112 in Europe. In France, dialing 112 will re-direct to the SAMU-Center 15 or the fire department, depending on the department. A call is first received by a medical assistant, and then an emergency physician or a general practitioner (depending on the severity of the case) decides on the appropriate response, from medical advice to the dispatch of an ambulance or a mobile intensive care unit (MICU).

With ∼300 thousand calls per year, the SAMU of the Gironde department (1.6 million inhabitants) is the second largest EMCC of France (Gil-Jardiné et al. 2021a). Its information system allows exhaustive archiving of all the calls and their treatment since 2005. This database is very large in terms of the number of records archived, but also in terms of the scope of the information it contains. Indeed, for each call, the origin of the call, the profile of the caller, the place of intervention, the reason for the call, the circumstances, the actions triggered by the call and the fate of the patient are reported. These data could be either standardized or in the form of free texts. Finally, there is information allowing both geolocation and clinical assessment (vital signs of the The aim of this study was to explore the usefulness of EMCC data in Public Health surveillance with relevant tools. To do so, we used data from the Gironde department. From the call reports, we applied deep learning methods for knowledge extraction. Transformer-based architectures were used to classify call reports according to the reason for the call or the pathology (influenza-like symptoms, chest pain, stroke, violence, etc.). We also extracted indicators that could serve as proxy measures for access to healthcare from text reports using a keyword-search algorithm. We then developed a dashboard visualization tool to perform a dynamic and spatial exploratory analysis of these data. Finally, we explored the reliability of the tool through two applications: detection of Covid-19 outbreaks and highlighting of territorial disparities in healthcare access.

Methods

Data sources

For all cases handled by the Gironde's EMCC, a clinical report is created in the form of a computerized free-text note in which the circumstances of the event at the origin of the call and the clinical observations are detailed. It is progressively updated by a medical assistant and a physician through the various telephone interactions with the patient/family/witnesses and, when applicable, with the medical or paramedical staff. Date and time are registered for every input. After removal of duplicate or empty clinical reports, ∼4 million remained over the period 2005 to 2020. A portion of clinical reports are annotated during the calls using a standardized diagnosis code (∼60%). We considered 13 broad categories for the main reasons of the call: chest pain, gastroenteritis and abdominal pain, flulike symptoms and breathing difficulties, focal neurological deficit and stroke, road traffic crash, violence, suicide and self-harm, injury other than violence, self-harm and road crash, pregnancy and delivery problems, malaise with loss of consciousness, stress and anxiety, and "other". For the validation purposes of the current study, a random sample of size ∼40 thousand from the unlabeled clinical reports of 2019 was manually annotated by emergency nurses (single annotation).

During a call, the address of the patient is registered systematically. Authorized hospital staff linked addresses to the official geographic code, a numeric indexing code used by the French National Institute for Statistics and Economic Studies (INSEE) to identify municipal councils.

If a MICU dispatch is required, times of departure and arrival on site are sent and recorded. On the other hand, vital parameters such as blood pressure are routinely measured at the arrival on site and sent to be included in the clinical report.

Only authorized hospital staff can have access to the call reports which contain PHI information (e.g., address). For use in this research, reports were automatically de-identified. We used a grep-based text-search procedure that was applicable because of the standardized format of personal information inserted in the reports.

Text classification and text extraction

A Transformer-based model [START_REF] Vaswani | Attention is all you need[END_REF]) was trained in two phases: a self-supervised phase using unlabeled examples followed by a supervised one using annotated examples. Text classification was initially performed on the 2016-2020 dataset. We used ∼900 thousand call reports corresponding to the period 2016-2018 in the selfsupervised phase. Of these, we used the ∼700 thousand that were manually annotated live during calls with a standardized diagnostic code. The 117-million parameters version of the GPT-2 model [START_REF] Wolf | HuggingFace's Transformers: State-of-the-art natural language processing[END_REF][START_REF] Xu | Pre-training a neural language model improves the sample efficiency of an emergency room classification model[END_REF]) was trained on a workstation with one Nvidia GeForce RTX Titan Graphic Processing Unit with 24GB of video random access memory. The sample of ∼40 thousand manually annotated reports of the year 2019 allowed to validate the trained and validated models on off-line annotated data.

The models were then applied to the ∼250 thousand labeled and unlabeled reports of the year 2020, providing a classification of reasons for calls. The confidence of the method was assessed using a bootstrap procedure with 10 thousand random partitions of the validation sample. For each partition and each category, the cut point was selected using the first half of the partition sample such that precision and recall were equal. The difference between the manually determined prevalence of the given category and the predicted prevalence was then computed in the second half of the sample. Median bias and 95% confidence interval were derived. The applied text classification methodology is detailed in a previous work (Gil-Jardiné et al. 2021b).

Data concerning time of departure and arrival of MICU were directly extracted from standardized variables in the EMCC database for the period 2010-2020.

Blood pressure measures are available in the free text of clinical notes. We used a regular expression search method to extract these data for the period 2010-2020. Regular expression search was also applied to symptoms. The negative forms were excluded. Misspelling and syntax variations were taken into account using the same method. The procedure applied to symptoms was previously developed and validated for a list of common symptoms on the 2005-2020 database (Gil-Jardiné et al. 2021a).

Dashboard development

We computed the mean number of calls for a given category (among the 13 defined above) at a given date by municipal council as the moving average number of calls over a period of user-defined length centered at the given date. We also considered the mean number of calls for a given category and a given municipal council among the mean number of calls for the given municipal council (being the mean number of calls a proxy of the dynamic population density). In order to compare two different categories, we also computed the ratio between mean number and proportion of calls. Finally, for these measures, we considered spatial correlation indices (global and local Moran's I statistics, with spatial weights matrix based on crow flies distances as well as driving time through the Open Source Routing Machine Application Programming Interface -OSRM API-).

To visualize these spatial indices and their evolution over time we developed a dashboard. The Shiny R package was used to build an interactive dashboard [START_REF] Chang | Shiny: Web Application Framework for R[END_REF]. Python was used for calls to APIs and processing of results as well as for the creation of analysis notebooks.

We used a choropleth map (a map fragmented into different polygons whose color changes according to the geographical data but not the size). The dashboard includes interactive features to select the indices (counts, proportions, etc.), the period of study, the date within this period, the length of the period, the call category. A reactive function allowed manipulating on-line data fast enough to keep the dynamic aspect of the dashboard when changing options. To make colors of the municipal councils change according to the quantitative indices, we used the r-Leaflet library (Cheng, Karambelkar, and Xie 2021), and downloaded the GeoJSON data of the municipal councils of Gironde on the france-geojson site. This site generates GeoJSON files of the French territory according to the scales and areas requested, with a JSON-like format describing various geometrical objects. We used a linear color scale to be able to compare municipal councils during epidemic (high number of calls everywhere) and non-epidemic periods. Interactive features also include a video export button that allows saving the changes in a video. The main purpose was to visualize the evolution of indices over time and to easily share it.

Applications

We explored the reliability of the tool through two applications: detection of Covid-19 outbreaks and highlighting of territorial disparities in healthcare access.

Detection of Covid-19 outbreaks Since our aim was to create consistent surveillance tools for new influenza-like outbreaks we explored if Covid-specific features could have been observed in near-real time from EMCC data. We considered calls related to flu-like symptoms and difficulty breathing at the first Covid-19 wave in Europe (late February 2020) and between 2016-2019 (a reference period used for comparison and for model-building). We chose not to analyze the presence of unconventional but Covid-specific symptoms, such as loss of taste and smell, because these symptoms took some time to emerge, so they may not have been reported early in the pandemic.

Highlighting territorial inequalities in access to healthcare In France, territorial inequality in access to health care (infrastructure, medical personnel, but also concentration of unfavorable socio-economic conditions in certain areas) is a complex problem for which it is difficult to provide objective evidence of its consequences.

We explored the spatial distribution of two indicators that could (directly or indirectly) measure territorial inequalities in access to healthcare. First, we computed the time between the departure and arrival on site of MICU. Second, we calculated whether a patient under care had high blood pressure (HBP), defined as a systolic blood pressure greater than or equal to 140 mmHg. The large portion of burden of diseases is attributed to HBP in the developing countries. HBP usually has no warning signs or symptoms, and many people are unaware that they have it until it is incidentally discovered by a healthcare professional. Awareness, treatment and control of HBP has been associated with socio-economic inequalities [START_REF] Palafox | Wealth and cardiovascular health: a cross-sectional study of wealthrelated inequalities in the awareness, treatment and control of hypertension in high-, middle-and low-income countries[END_REF][START_REF] Anstey | Income inequality and hypertension control[END_REF]. HBP served as a proxy measure for access to healthcare.

Results

Text classification and text extraction

Deep-learning text classification achieved a Max-F1 score ranging from 0.48 to 0.80, and an area under ROC curve ranging from 0.79 to 0.99 on the validation dataset. The 3 classes achieving the best Max-F1 scores among the 13 classes were "pregnancy and delivery problems", "chest pain", and "road traffic crash" (scores of 0.80). The 3 classes worst performing were "malaise with loss of consciousness", "stress and anxiety", and "violence" (scores of 0.48, 0.49, and 0.64, respectively). Text classification results are detailed in a previous work (Gil-Jardiné et al. 2021b).

Keyword search applied to symptoms achieved a Cohen's kappa ranging from 59 to 75 for the following symptoms: agueusia, anosmia, cough, fever, muscle soreness, dyspnea. Text extraction results are detailed in a previous work (Gil-Jardiné et al. 2021a).

Dashboard

The current version of the dashboard is a pilot edition developed to explore the usefulness of EMCC data in Public Health surveillance. The Gironde department has 535 municipal councils that are represented in the interactive map dashboard. The number of calls over time is also represented by a time series graph. The dashboard contains 4 main sections detailed in Figure (2): (i) "Filter," (ii) "Variables," (iii) "Indices," and (iv) "Video." The Filter section allows the user to select inputs such as the period of study, a date within this period, the length of the period, the minimum number of calls of municipal councils to be represented, and the age range. In the Variable section, the user selects symptoms or call reasons to appear on graphs. In the Indices section, the Figure 2: The dashboard to visualize spatial distribution and the number of calls over time with interactive features. Interactive features allows to select, for example, the indices to be spatially represented, the period of study, the date within this period, the length of the period, the minimum number of calls per municipal council, video export options, the call category and age range. Data correspond to flu-like symptoms and difficulty breathing calls during the first Covid-19 wave in Europe (left) and time (mean values over time per municipal council) to get to the site by mobile intensive care units (right).

indices to be spatially represented (counts, proportions, ratios, means, medians, etc) are selected. Finally, the Video page also lets the user select the video export options. Overall, the user therefore has a role in filtering the data and in deciding which information is represented. Figure (2), right, shows the spatial distribution of time (mean values over the period 2010-2020 per municipal council) to get to the site by MICU. This map suggests that the closer to the hospital centers, which are located in urban areas, the sooner the MICU arrive.

Applications

Figure (3) shows the proportion of HBP by municipal council for the period 2010-2020 (left) and the median yearly income in EUR by municipal council for the year 2015 (right). There is not a perfect match between both maps, we observe a tendency: the northern and eastern periphery generally show high proportions of HBP and low median incomes.

Conclusion

Disease surveillance in public health has received increased attention over the past two years. Early identification of communicable disease outbreaks can be beneficial in addressing many aspects of public health. Public health surveillance based on EMCC call reports appears to be feasible. However, the sensitivity and specificity of the EMCC signal appear to be variable and the quality of the results depends on the health event of interest. The EMCC data used in this work has the potential to be beneficial in these efforts if used properly.

In the Covid-19 application, we were faced with detecting weak signals in intense noise. The exclusion of novel disease-specific symptoms, such as loss of taste and smell, when reviewing the initial Covid data is questionable: these disease-specific symptoms are likely to be valuable once established. This work was conducted as part of a Covid-19 epidemiologic surveillance research project. However, the same tools could be applied to analyze seasonal patterns of the same symptoms in relation to influenza prevalence. By leveraging the extensive data available over time, these results could be more representative in predicting baseline seasonal resource needs, and provide additional differentiation, in an observed versus predicted manner, for the incremental call volume attributable to the pandemic era compared with the pre-pandemic era.

Indicators that can directly measure or serve as proxy measures of access to care recorded over a long period of time have suggested territorial inequalities in access to care. Patient outcomes can obviously be impacted by the time it takes for help to arrive. In addition to territorial inequalities in access to care, other important elements (currency and volume of skills, cost-effectiveness, limited and expensive MICU resources) may help explain the observed differences. On the other hand, people living in low-income municipal councils may have a higher risk of being unaware that they have BPH, which may be related to poorer access to healthcare professionals. The use of an isolated blood pressure during a period of heightened stress (experience of an event sufficient to contact the EMCC) should be considered a screen- ing rather than a diagnostic tool. Nevertheless, in the event that there is no association between suspected BPH and the municipal council, we should observe a similar distribution across the map.

This paper has illustrated the feasibility of implementing R Shiny to represent EMCC data to authorized end users. The current version of the dashboard is a pilot edition. It highlights the utility of EMCC data in public health surveillance [START_REF] Yoon | Using syndromic surveillance for all-hazards public health surveillance: Successes, challenges, and the future[END_REF]. Visualizing EMCC data in near-real time through online dashboards is a pragmatic way to meet the epidemiology community's demand for upto-date information. Scaling up such a tool to the national level will pose both technical and administrative challenges, particularly related to the current highly decentralized management of data from the French health centers.

Declarations

This work complies with the application framework provided by Article 65-2 of the amended French Data Protection Act and the General Regulation on the protection of personal data in terms of the protection of personal health data and the protection of privacy. It was approved by the head of the emergency department of the Bordeaux University Hospital.

Figure 1 :

 1 Figure 1: Data-flow diagram. Only authorized hospital staff can have access to the call reports which contain PHI information. Data used for the dashboard (last line of the dataflow) were de-identified.

  Figure (1) shows data-flow diagram.

Figure ( 2

 2 Figure (2), left, shows the spatial distribution of the number of calls corresponding to flu-like symptoms and difficulty in breathing across all calls during the beginning of the first Covid-19 wave in Europe. The total number of calls is used as a proxy of the dynamic population density. Despite the fact that the Gironde department was little affected (in terms of hospitalized patients, since Covid-19 tests were not yet available) during the first wave, the proportion of flulike symptoms and difficulty breathing is high almost everywhere. Regardless of the indicator used and the comparison made with the 2016-2019 years, no particular feature was detected.Figure(2), right, shows the spatial distribution of time (mean values over the period 2010-2020 per municipal council) to get to the site by MICU. This map suggests that the closer to the hospital centers, which are located in urban areas, the sooner the MICU arrive.Figure(3) shows the proportion of HBP by municipal council for the period 2010-2020 (left) and the median yearly income in EUR by municipal council for the year 2015 (right). There is not a perfect match between both maps, we observe a tendency: the northern and eastern periphery generally show high proportions of HBP and low median incomes.

Figure 3 :

 3 Figure 3: Proportion of HBP (number of HBP across all measured blood pressures) by municipal council for the period 2010-2020 (left) and median yearly income in EUR by municipal council for the year 2015 (right). Cut-off values were based on deciles.

Acknowledgments

We thank the anonymous reviewers for their insightful comments and suggestions.

Funding This work was supported by the French National Research Agency (ANR) under the grant COSAM "Epidemiological surveillance of the COVID-19 pandemic period by real-time automatic classification of clinical notes from 15 emergency call centers using Transformer-based artificial neural networks" (project number ANR-20-COVl-0004-01). The authors' research teams had annual grants from the University of Bordeaux, INSERM U1219 and INRIA.