Anton Ashuiev

Matthieu Humbert

David Gajan

Sebastien Norsic

Jan Blahut

Keith Searles

Daniel Klose

Anne Lesage

Guido Pintacuda

Jean Raynaud
email: jean.raynaud@univ-lyon1.fr

Jean Blahut

Anne Lesagee

Vincent Monteil
email: vincent.monteil@univ-lyon1.fr

Christophe Coperet
email: ccoperet@inorg.chem.ethz.ch

Gunnar Jeschke
email: gunnar.jeschke@phys.chem.ethz.ch

J Blahut

C Copéret

Spectroscopic Signature and Structure of Active Centers in Ziegler-Natta Polymerization Catalysts revealed by Electron Paramagnetic Resonance

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Nowadays, polymers and particularly plastics are the most common products of chemical industry, comprising nearly 80% of the worldwide industry output (1). Two important polymers, which are widely used for e.g. packaging and construction materials in modern times, are polyethylene and polypropylene; they together sum up to more than half of the total plastics production (2). Such mass production of polyethylene and polypropylene became possible only after discovery of transition metal-based catalysts in the second half of the 20 th century, such as Ziegler-Natta catalysts (3,4). With their ability to promote the synthesis of high-density linear polyethylene and stereoregular polypropylene under relatively mild conditions, the Ziegler-Natta catalysts are responsible nowadays for nearly 50% of worldwide production of polyethylene and around 95% of overall polypropylene production (5). The boom of their commercial usage arose with the development of heterogeneous MgCl2-supported Ziegler-Natta catalysts (TiCl4/MgCl2 activated with Et3Al) in the 1960s, which appeared to be nearly 100 times more active than the ones originally proposed by Ziegler and Natta (5,6). Due to the enormous productivity of such catalysts and their unprecedented ability to tune the properties of produced plastics in a controlled way, the supported Ziegler-Natta catalysts became a workhorse of modern polyolefin industry (6).

Knowledge of the structure of the corresponding active center(s) represents a crucial step for enabling further improvement of the Ziegler-Natta catalytic process. However, despite decades of extensive studies, the atomic-scale description of the catalyst active centers/sites (without or with coordinated monomer) remained so far elusive. Ziegler-Natta catalysts are typically prepared by impregnating a MgCl2 support with TiCl4 that needs to be first treated either mechanically (grinding/ball milling) or chemically with Lewis bases of different types, often oxygen-based such as diethers, alcohols, or THF (6,7). The catalytic Ziegler-Natta polymerization then requires the activation of the obtained precatalyst with an alkylaluminium reagent acting as a co-catalyst, e.g. Et3Al. According to the proposition of Cossee and Arlman (8), the active site shall include a metalcarbon bond in order to perform ethylene insertion, which points towards Ti alkyl species as the active centers; alkylation of TiCl4 most likely occurs upon addition of aluminum alkyls (Fig. 1).

Based on the activity of homogeneous and supported group IV transition-metal metallocenes towards ethylene polymerization (9 -12), for which cationic M(IV) alkyl species were found to be the active sites (13), Ti(IV) cationic alkyls (A, Fig. 1a) have been proposed as possible active centers of MgCl2-supported Ziegler-Natta catalysts (14). On the other hand, the addition of aluminum alkyls leads to a reduction of a large amount of Ti(IV) (15). Based on the estimated correlation of polymerization activity with the general amount of Ti 3+ species (16), Ti(III) alkyls (B, Fig. 1a) have been proposed to be the active species (17). This proposition is further supported by a recent finding that well-defined Ti(III) neutral alkyls are efficient ethylene polymerization catalysts, where the presence of an unpaired electron favors C2H4 polymerization (18), and by the activity of silica-supported Ti(III) hydrides towards ethylene polymerization (19,20). In fact, Giulio Natta himself proposed that the Ziegler-Natta catalysts could be bimetallic Ti(III)-Al complexes containing organometallic bonds (21) that could be interpreted as Ti(III)-(μ2-Cl)-(μ2-R)-AlR2 type (C, Fig. 1a). This structure is also the cornerstone of the Rodriguez-Van Looy mechanism (22), which extends upon Cossee & Arlman's and proposes a bimetallic species to explain the stereospecificity of ZN-catalyzed propylene polymerization (22). So far, however, there has been no direct evidence for any of these proposed structures.

The difficulty of spectroscopic characterization of the active species of heterogeneous Ziegler-Natta catalysts is mostly related to the small amounts of those sites compared to the overall Ti amount. This problem may be addressed by investigating catalysts treated with strong Lewis acids, e.g. BCl3 (Fig. 1b), which has been shown to significantly enhance the catalytic activity and therefore the number of active sites (23,24), thereby opening new opportunities to capture spectroscopic signatures. Here, we report the structural characterization by EPR and NMR spectroscopies of these highly active Ziegler-Natta catalysts for ethylene polymerization treated with different amounts of BCl3. Figure 1a shows schematically the protocol used to prepare the ZN samples used in this study.

Spectroscopic identification of the active species of Ziegler-Natta heterogeneous catalysts

As previously observed (24), the addition of BCl3 continuously increases the activity of the Ziegler-Natta catalyst from 9 kgPE . (gcat . h) -1 up to the maximal value of 38 kgPE . (gcat . h) -1 (Fig. 2a and Table S1). At the same time, the average molar masses Mn and Mw as well as the corresponding dispersity Đ of produced polyethylene remain essentially the same upon BCl3 addition. This is strong evidence that the structure of the active centers of the Ziegler-Natta catalysts is not changed and that the active-site concentration increases with addition of BCl3, as discussed in a previous investigation based on a combination of kinetics and polymerization results (24). The activation by Et3Al of samples prepared with different BCl3 loading (Fig. 1b) was studied by NMR and pulse EPR spectroscopies (see SM Materials and Methods). A low [Al]/[Ti] ratio (~10), ca. one order of magnitude lower than typical ratios employed in ZN-catalyzed polymerizations -100 < [Al]/[Ti] < 400 (24), -was used for these experiments for practicality. Higher ratios are used for conventional polymerization runs to scavenge impurities from the reaction medium (present in monomer, solvents…) and to maintain high activities throughout the polymerization processes. To ensure that the observed spectroscopic signatures actually relate to active centers, which become active sites in the presence of ethylene, we verified that all alkylated materials prepared under the conditions for the NMR and EPR experiments also displayed the expected polymerization performances (see SM Validation of Spectroscopic Methodology for details, and Table S2).

We first investigated the samples via solid-state NMR. While the 11 B solid-state NMR spectrum of precatalysts (before Et3Al addition) shows that all B-containing species are boron alkoxides, resulting from the reaction of BCl3 with either THF or chlorobutanoxy ligands (Fig. S1) (7), a sharp signal associated with physisorbed triethylboron formation (BEt3) is observed after activation with Et3Al (Fig. S2). The addition of Et3Al also leads to the appearance of paramagnetic signals at ca. -60 ppm in the 1 H solid-state NMR spectra (25) of Ziegler-Natta catalysts prepared with or without BCl3 addition, that are likely related to the presence of Ti III species in their vicinity (Fig. S3). These paramagnetic signals are noticeably more pronounced in the spectra of the BCl3treated samples (Fig. S3).

Consequently, we tried to identify these paramagnetic species by EPR spectroscopy at 10 K and 9.5 GHz on samples with different BCl3 loading, i.e. B/Ti = 0, 2.60 and 4.10 (Fig. 2b). These spectra reveal two paramagnetic species, characterized by a broad line around 340-390 mT and a narrower line at 320-340 mT that corresponds to an axially symmetric species. A species with a signal very similar to the 340-390 mT band has been observed before and has been assigned to Ti(III) surface species on MgCl2(110) (26). Only the 320-340 mT band grows with increasing amount of added BCl3, but is already present as a weak signal without addition of BCl3 (see Fig. 2c and Fig. S4 -S6). Each of the three CW EPR spectra, shown in Fig. 2b, was fitted by a superposition of four individual spectra, revealing nearly the same g tensor parameters for all samples (Table S3). Three mean sets of principal g values contributing to the 340-390 mT band are g1 = [1.8167 1.8657 1.9156], g2 = [1.8910 1.9402 1.9621], g3 = [1.9642 1.9716 1.9902]. They are consistent with previously estimated g tensor parameters for the three different conformations of TiCl3 on the different MgCl2 surfaces (26).

We then focused on establishing the nature of the species with the nearly axially symmetric g tensor g4 = [2.0023 2.0053 2.1289]. Being larger than or similar to the free electron g value (ge), these principal g values are highly unusual for a Ti(III) species. On the other hand, the large g anisotropy makes assignment to an organic radical very unlikely. The g tensor with unexpectedly large principal values is reminiscent of a previous observation for the 3d 1 3,10,17,24tetrasulphonatophthalocyanin (tspc) complex of V(IV)O, in particular, with its g|| > ge (27), which indicates that the g > ge values for d 1 species are indeed possible (see also SM g tensor calculations). This, together with the large anisotropy, led us to attribute the observed species to a Ti(III) complex with a distinct electronic structure rather than to an organic radical. A similar species can be recognized as a weak contribution to Q-band EPR spectra that were obtained in previous work on catalysts that were not activated by BCl3 (26), but this species has not been further discussed. We found that it was strongly suppressed in 35.7 GHz pulse EPR echo-detected spectra (Fig. S4), whereas it was readily observed in 9.5 GHz echo-detected EPR spectra (Fig. 2c). Since strong suppression of the TiCl3 echo signals at 9.5 GHz is beneficial for characterizing the g > 2 species, further pulse EPR studies of the samples of MgCl2-supported Ziegler-Natta catalysts with different BCl3 loading were performed at this frequency (Fig. 2c). Like the CW EPR of this species, the echo-detected EPR signal, shown in Fig. 2c, continuously grew in intensity with increasing BCl3 loading and thus paralleled the increasing catalyst activity (Fig. 2a). Therefore, we identify this g > 2 species (Fig. 2 b &c, marked with arrows) as the active centers of MgCl2supported Ziegler-Natta catalysts.

Evaluation of structure of the active centers

We further characterized this species by the 2D hyperfine spectroscopy technique HYSCORE (28). For these studies, we selected the sample with the highest activity of 38 kgPE . (gcat . h) -1 (B/Ti = 3 and Table S1) because of the presence of the highest concentration of active centers. The HYSCORE spectrum was measured at the maximum of the 9.5 GHz field-swept echo-detected EPR spectrum (Fig. 3a, marked with arrow). HYSCORE spectroscopy (Fig. 3b) reveals the presence of 1 H and 27 Al nuclei, coupled to the paramagnetic center by magnetic hyperfine interaction. The experimental HYSCORE pattern was simulated by adjusting hyperfine and quadrupole coupling parameters (Fig. 3b,red). The estimated 1 H isotropic hyperfine coupling of aiso ( 1 H) = -7.5 ± 0.5 MHz is, in fact, typical for the protons of alkyl ligands in Ti(III) alkyl complexes (18,20). Within a point-dipole approximation we find a relatively large average Ti … H distance of rTi-H = 3.16 ± 0.24 Å, which suggests that only relatively weak α-or β-H agostic interactions are observed within the alkyl ligand(s) (18,20). At the same time, the moderate 27 Al isotropic hyperfine coupling aiso ( 27 Al) = 1.5 ± 0.2 MHz indicates that the Al atom is in a proximity to the paramagnetic center, most likely being connected to it through one bridging atom (see SM Evaluation of the structure of the active species). Strong 27 Al quadrupole coupling Q ( 27 Al) = -42 ± 1 MHz indicates low symmetry of the Al coordination geometry. No 11 B hyperfine couplings were observed in the HYSCORE spectra. This is consistent with the absence of any modifications of the active center upon BCl3 addition that can be inferred from the similar features of the produced polyethylenes as previously mentioned (24), from the removal of B in the form of BEt3 and from the unchanged g tensor principal values. The EPR data also agrees with the NMR data discussed above, namely the observation of 1 H paramagnetic NMR resonances (Fig. S3). Very importantly, all these materials with characteristic EPR Ti(III)-alkyl signatures are active in polymerization by sole addition of ethylene monomer (see Table S2). These EPR spectroscopic signatures are thus assigned to active centers.

Based on these conclusions, we performed a search through possible DFT models, including Ti(III) alkyls, bridging alkyls and alkoxides with various ways of coordination of indicated organometallic ligands and aluminum alkyls (Fig. S8 -S11). Among all the tested models, we found only one (Fig. 3c & S11, a), that provides DFT-calculated 1 H hyperfine couplings for all H atoms of the Ti-C2H5 ligand that are in line with the experimental 1 H HYSCORE pattern (Figs. 3d & S11, b -f), together with a reasonable fit of the DFT-computed 27 Al couplings to the ones observed experimentally (Table S4). As 1 H hyperfine couplings were previously found to act as fingerprints of the conformations of Ti(III) alkyl complexes (18,20), we propose the structure, shown in Fig. 3c, as an active center of the Ziegler-Natta catalysts. Being essentially a Ti(III) metal-alkyl complex of a formula [Ti(C2H5)Cl2-(μ-Cl)-Al(C2H5)2]@MgCl2 (Fig. 3c), this structure is in fact in line with the original proposal of Giulio Natta regarding the active site of Ziegler-Natta catalysts (21). With a calculated Ti-C-H angle of 98.64°, it displays an α-H agostic interaction. The Al atom is essentially tris-coordinated, except for a weak Al-H interaction (2.183 Å distance) with the β-H atom of Ti-C2H5 ligand. A similar structure of [Ti(C2H5)Cl2-(μ-Cl)-(μ-C)-Al(C2H5)2]@MgCl2 (C, Fig. 1a & Fig. S9,b), which has the α-C atom of Ti-C2H5 ligand bridging between the Ti and Al atoms, is calculated to be slightly more stable by 4.0 kcal•mol -1 according to the DFT computation, but exhibits a strong mismatch between DFT-computed 1 H hyperfine couplings and the experimental HYSCORE spectrum (see Fig. S9,b). The former is likely more favorable experimentally due to specific local environments that are not included in our cluster model that was used to allow accurate EPR calculation but only takes into account the first coordination environment.

Having also a weak π character in the Ti-C bond, revealed by a deviation of the natural hybrid orbital (NHO) on carbon from the Ti-C axis (ΘNHO-C-Ti = 9.0°), the evaluated structure of the active species (Fig. 3c & Fig. S11,a) is akin to β-diiminato Ti(III) alkyls, which were shown to participate in ethylene polymerization in the absence of co-activator (18). Such π character, being likely enhanced after ethylene coordination, favors ethylene insertion into the Ti-C bond, while electron transfer of unpaired electron density from SOMO to the π*(C2H4) orbital lowers the formation energy of π-ethylene complexes in d 1 metal alkyls (18). These factors make Ti(III) d 1 alkyl complexes efficient ethylene polymerization catalysts, which supports our proposition of [Ti(C2H5)Cl2-(μ-Cl)-Al(C2H5)2]@MgCl2 species (Fig. 3c) to be the active centers of the Ziegler-Natta catalysts that become the active sites for polymerization in the presence of ethylene.

Materials and Methods

All experiments were carried out under inert conditions.

Synthesis of heterogeneous Ziegler-Natta catalysts The MgCl2 . THF support was prepared by stirring MgCl2 in boiling THF (with a ratio of 1 g per 7 mL of solvent) for 4 h. Heptane was added to the mixture and the precipitating/recrystallizing solid was washed three times with heptane and dried under vacuum at 90 °C for 1 h to yield a white solid corresponding to MgCl2(THF)1.5 (or "activated support"). The elemental analysis of the MgCl2 . THF support showed that no traces of B were introduced before the treatment using the described procedure (vide infra).

The MgCl2 . THF support was immersed in pure TiCl4 and the mixture was heated at 90°C during 4 hours under stirring. Afterwards, three steps of hot toluene washings, one step of hot 1,2dichloroethane washing and one step of heptane washing at room temperature were realized. Then, a second treatment with diluted TiCl4 in heptane was performed for 2 h at 90 °C, followed by several washings in heptane (min. 3) at room temperature in order to remove TiCl4 excess.

For the catalysts modified with BCl3, these steps were followed by introduction of different amounts of 1 M solution of BCl3 in heptane to the previously obtained solid, heating to 90°C during 1 hour under stirring and three steps of washing with heptane. After drying under vacuum, the obtained samples were kept under inert atmosphere (glovebox).

The BCl3 loading of all the prepared samples, verified by the elemental analysis, together with their activities towards ethylene polymerization and the parameters of produced polyethylene, are shown in Table S1. The samples with B/Ti = 0, B/Ti = 2.60 and B/Ti = 4.10, used in quantitative pulse EPR measurements, were prepared from the same original batch, while all the other samples were prepared independently. In particular sample with B/Ti = 3 (see Table S1) shows the good reproducibility of the inorganic synthesis. Since its [Ti] content was slightly higher than that of B/Ti = 4.10 for instance, it explains that it displayed the highest activity by weight of all tested catalysts and thus the highest corresponding characteristic signal intensity in EPR spectroscopy.

All elemental analyses were done by Mikroanalytischen Laboratorium Kolbe in Oberhausen (Germany).

Synthesis of polyethylenes

5-10 mg of precatalyst (a precise mass was used) was added to a 1 L flask containing a 400 mL volume of a 3 mmol•L -1 Et3Al solution in heptane under Ar atmosphere. The mixture was then transferred to a 1 L-autoclave reactor. The ratio Al/Ti varied between 100 and 400 in these experiments. After that, 1 bar of dihydrogen and then 6 bar of ethylene were subsequently added in the reactor and the polymerization was performed at 80 °C. Polymerizations were stopped after a 5-bar consumption of ethylene from the 2 L ballast that feeds the reactor corresponding to about 30 g of polyethylene, which we consistently verified gravimetrically. The obtained polymer, already precipitated after the slurry polymerization, was washed with methanol and filtered through sintered glass.

All activities were calculated by measuring ethylene consumption during time using Van der Waals' cubic state equation.

SEC: Molar masses (Mw and Ɖ) of the polyethylenes were determined by size exclusion chromatography (SEC) with a Viscotek system (Malvern Instruments, Malvern, UK) equipped S3 with three columns (Polefin 300mm×8mm I. D. from Polymer Standards Service, porosity of 1 000, 100 000 and 1 000 000 Å). Sample solutions of 1 mg•mL -1 were eluted in 1,2,4trichlorobenzene using a flow rate of 1 mL•min -1 at 150 °C. The mobile phase was stabilized with 2,6-di(tert-butyl)-4-methylphenol. Three detectors were used: viscosimeter, refractometer and Light Scattering detector. The system was calibrated with polystyrene standards using triple calibration.

Sample preparation for NMR studies

For 11 B NMR studies of the samples before the addition of Et3Al ("precatalysts", Fig. S1), the solid powder of the precatalysts was used as it was.

For 1 H and 11 B NMR studies of the samples after the addition of co-catalyst Et3Al (Figs. S2 &S3), all the samples were activated with 1 M solution of Et3Al in C6D6 with a ratio Al/Ti = 10. Two samples were studied: one in the absence of BCl3 (no BCl3 treatment, Table S1, entry 1) and one with a ratio B/Ti = 1.35 (Table S1, entry 2).The obtained "pasty" suspensions were merely filtered with the aim of reaching powder samples which were directly inserted in a 1.3 mm zirconium-oxide rotor. All these stages were performed in a glovebox.

Validation of Spectroscopic Methodology: Polymerization performances of alkylated NMR samples

The two samples (Table S1, entry 1 and 2) were activated in a glovebox with 1 M solution of Et3Al in C6D6 with Al/Ti = 10. The thus-obtained suspensions were quickly filtered with the aim of obtaining activated powders. The resulting pasty solids were directly added to heptane solution (to recover a classical suspension for a catalytic heterogeneous polymerization of ethylene) with Et3Al (concentration of 3 mmol/L) to match the standard value of 100 < Al/Ti < 400) or without Et3Al (to verify the hypothesis that active centers observed spectroscopically turn to polymerization active sites by sole addition of ethylene monomer) and tested in polyethylene synthesis. Standard ethylene polymerization conditions were used with or without H2 addition. Activities were on par with expectations from Ziegler-Natta samples and are listed in Table S2-a.

Paramagnetic NMR measurements

All experiments were carried out on a wide-bore Bruker Advance III with a 1 H Larmor frequency of 500 MHz using a double-resonance 1.3 mm probe at a spinning frequency of 60 kHz. Probe temperature was controlled with a Bruker BCU II unit to 270 K, which corresponds to a temperature of ~315 K inside the sample due to frictional heating. Ultra-dry, oxygen-free nitrogen obtained by evaporation of liquid nitrogen was used for sample spinning and temperature regulation.

1 H chemical shifts were referenced to the single resonance observed for protons in adamantane at 1.8 ppm with respect to the signal for neat tetramethylsilane (TMS).

The 1 H NMR spectrum of the sample without BCl3 (B/Ti = 0) after Et3Al addition (Fig. S3, a) was acquired with a saturation-recovery pulse sequence, using a 5 ms delay between the train of saturation pulses and the read pulse. A rotor-synchronized spin-echo period of total length two times 16.67 µs was inserted before acquisition. The proton pulses were applied at a RF field of 200 kHz. A total of 131 072 scans were accumulated.

The 1 H NMR spectrum for the sample with BCl3 (B/Ti = 1.35) after Et3Al addition (Fig. S3,b) was acquired with a saturation-recovery pulse sequence, using a 1 ms delay between the train of saturation pulses and the read pulse. A rotor-synchronized spin-echo period of total length two S4 times 16.67 µs was inserted before acquisition. The proton pulses were applied at a RF field of 250 kHz. A total of 40 960 scans were accumulated. 11

B NMR spectroscopy

The chemical shift was referenced to the single resonance observed for B(OH)3 in solution at 19.6 ppm.

The 11 B NMR spectrum for the precatalyst with BCl3 (B/Ti = 1.35, Fig. S1) was acquired with a direct excitation pulse sequence, composed of a 90° excitation pulse followed by a rotorsynchronized spin-echo period of total length two times 16.67 µs. During acquisition heteronuclear proton decoupling was applied at a 100 kHz RF field strength. A total of 5 120 scans were accumulated. The RF field for the 180° 11 B pulse was 25 kHz.

The 2D 11 B-11 B MQMAS spectrum was recorded with 256 t1 increments 40 µs, each with 240 scans. The RF field was 270 kHz for excitation and conversion pulses, and 10 kHz for the 90° selective pulse.

The 11 B NMR spectrum for the sample with BCl3 (B/Ti = 1.35) after Et3Al addition (Fig. S2) was acquired with a direct excitation pulse sequence, composed of a 90° excitation pulse followed by a rotor-synchronized spin-echo period of total length two times 16.67 µs.. A total 20 480 scans were accumulated. The RF field for the 180° 11 B pulse was 344 kHz, and no proton decoupling was applied during signal acquisition.

Sample preparation for EPR studies

All samples for EPR spectroscopy were prepared in quartz tubes. The solid powders (~40-50 mg) of the catalysts were put into the EPR tubes inside the glovebox, filling the tubes up to 1.5 -2.5 cm height. The co-catalyst Et3Al was added to the solid powders of the catalysts directly in EPR tubes inside the glovebox as a 1 M toluene solution. Toluene was used for its good glassforming abilities; it was purified using double semi-permeable surface (SPS) alumina columns (MBraun, Garching, Germany), and was degassed using three freeze-pump-thaw cycles before being used. For all samples, the Et3Al was added in the quantities enough to grant its molar excess towards Ti (5-10:1). Afterwards, all EPR tubes were flame-sealed under high vacuum conditions (10 -5 mbar) while having the catalyst and the solvent frozen in liquid N2. The sealed EPR tubes were centrifuged and were frozen in liquid N2 several times in order to remove gas bubbles from the solid part of the sample. All the samples were prepared in the indicated way directly before the EPR measurement; the general time between the activation with Et3Al and the beginning of EPR measurements was around 20 minutes. A special procedure of sample preparation for the quantitative EPR measurements was applied (vide infra).

Validation of Spectroscopic Methodology: Polymerization performances of alkylated EPR samples

The two samples (Table S1, entry 1 and 5) were activated in a glovebox with 1 M solution of Et3Al in toluene with Al/Ti = 7. The obtained suspensions were directly added to a heptane solution either with Et3Al (concentration of 3 mmol/L) to match the standard value of 100 < Al/Ti < 400) or without Et3Al to verify the hypothesis that active centers observed spectroscopically turn to polymerization active sites by sole addition of ethylene monomer. Both samples were tested in polyethylene synthesis. Standard ethylene polymerization conditions were used with or without H2 addition. Activities were on par with expectations from Ziegler-Natta samples and are listed in Table S2-b.

S5 CW EPR measurements

All CW EPR spectra were recorded at the temperature of 10 K at Bruker Elexsys E680 EPR spectrometer, equipped with helium flow cryostats ESR900 (Oxford Instruments, Oxfordshire, UK). CW measurements were performed using a Bruker SHQ resonator, with a modulation amplitude of 0.1 mT (Fig. 2b & Fig. S5) or 0.4 mT (Fig. 3a) and modulation frequency of 100 kHz. The lock-in amplifier time constant and conversion time were set to 81.92 ms and 327.68 ms, respectively (Fig. 2b & Fig. S5) or 81.92 ms and 167.77 ms, respectively (Fig. 3a). The correction of the magnetic field offset was done using DPPH (Sigma-Aldrich, Buchs, Switzerland) as a standard.

Field values of all the spectra, shown on Fig. 2b, are normalized by the microwave frequency ratios. The intensities of all shown spectra are normalized by the values of their double integrals for qualitative illustration of the change in the CW EPR spectra with BCl3 addition. Field values of CW EPR spectrum of the catalyst with B/Ti = 3 (Fig. 3a, black) are corrected by the microwave frequency ratio in order to match the echo-detected EPR spectrum (Fig. 3a,red).

Analysis of EPR spectra

Experimental EPR spectra were analyzed in a first step by global least squares fitting of all three CW EPR spectra (Fig. 2b) together with the echo-detected EPR spectrum of the sample with B/Ti = 4.10 (Fig. 2c, red), using the EasySpin toolbox (1). Additional estimates for the g tensor principal values of all three TiCl3 species and of the active species were estimated via least square fitting of individual CW EPR spectra in EasySpin (1). The final set of g tensor parameters of the active species was obtained from the fit of 9.5 GHz echo-detected EPR spectrum of the sample of Ziegler-Natta catalyst with B/Ti = 4.1 (Fig. S5,a). For the g tensor parameters of the TiCl3 species we report average values, estimated from independent least square fits of all three CW EPR spectra (Fig. S5, b -d). For these final fits, the g tensor parameters of the active species were fixed to the values obtained from the fit of the echo-detected EPR spectrum. Because of the low relative weight of the active species in the CW EPR spectra such stabilization of the fits is beneficial. The g tensor parameters of all the species present in CW EPR spectra are summarized in Table S3. 9.5 GHz echo-detected EPR measurements All 9.5 GHz echo-detected EPR spectra were recorded at a temperature of 10 K with a Bruker Elexsys E680 EPR spectrometer, using a Bruker MS3 split-ring resonator. All the spectra were recorded using a standard 2-pulse sequence π/2 -τ-π -τ-echo; the power of microwave pulses was set up for pulse lengths tπ/2 = 16 ns, tπ = 32 ns by observing the inversion of electronic magnetization in Rabi oscillations. All 9.5 GHz echo-detected EPR measurements, except for the quantitative ones (Fig. 2c), were performed with an interpulse delay τ = 128 ns; for quantitative pulse measurements, the delay was set to τ = 144 ns.

GHz echo-detected EPR measurements

All 35.7 GHz echo-detected EPR spectra (Fig. S4) were recorded at a temperature of 10 K with a home-built Q-band spectrometer (2), using a home-built 3 mm Q-band resonator (3). All the spectra were recorded using a standard 2-pulse sequence π/2 -τ-π -τ-echo; the power of microwave pulses was set up for pulse lengths tπ/2 = 12 ns, tπ = 24 ns by observing the inversion of electronic magnetization in Rabi oscillations. An interpulse delay τ = 200 ns was used for all hyperfine and 27 Al hyperfine and quadrupole couplings (Fig. 3b), or DFT computed parameters of 1 H hyperfine tensors (Fig. 3d & Figs. S8 -S11). Spectra were simulated for interpulse delays τ = 128 ns, τ = 160 ns and τ = 192 ns, assuming infinite bandwidth of microwave pulses. The parameters of hyperfine couplings are described through the principal values of hyperfine tensors in the form A = aiso + adip = [aiso + adip(1); aiso + adip(2); aiso + adip (3)]. The aiso value designates the isotropic part of the hyperfine tensor, associated with Fermi contact interaction, while adip = [adip(1); adip(2); adip(3)] designates the anisotropic part of the hyperfine tensor, associated with dipole-dipole interactions. In manual simulation of 1 H hyperfine couplings, the anisotropic part was presented as adip = [-Tdip; -Tdip; 2•Tdip], where Tdip is the constant of dipole-dipole interaction. This assumes strict localization of the electron spin on Ti 3+ (point-dipole approximation). The Ti 3+ -1 H distance can then be estimated through the following formula:

rTi-H = ! " # " $ % & ℏ () * + ,-.

/

The 27 Al quadrupole coupling was defined through the quadrupole coupling constant P and the rhombicity of quadrupole tensor η.

DFT-based geometry optimizations

All geometry optimizations, including the optimization of the main model for the active species (Fig. 3c) and all the other tested models (Fig. S8 -S10) were performed with the ORCA 3 (4) program. The unrestricted Kohn-Sham formalism with the PBE0 functional ( 5) was used, together with Grimme's three-center dispersion correction ( 6) and the COSMO (7) continuum solvation model for toluene. For all the optimizations, the RIJCOSX approximation ( 8) was applied, together with an auxiliary basis set (9) def2-TZVPP/J and increased numerical precision of COSX integration (IntAccX 7,7,7; GridX 7,7,7 in ORCA convention).

For all developed models, the MgCl2 surface was represented as a {Mg8Cl16} cluster, following previous work (10). During the optimizations, the cluster coordinates were fixed, while having the first Ti(III) coordination sphere and all the other atoms fully relaxed.

DFT-based calculation of EPR parameters

All 1 H hyperfine and 27 Al hyperfine and quadrupole couplings (Fig. 3c; Fig. S8 -S11) were calculated based on previously optimized structures. The calculations were performed in ORCA 4 (11), using the unrestricted Kohn-Sham formalism with PBE0 (5) together with Grimme's threecenter dispersion correction (6). Note that the dispersion correction affects only energy, but not the DFT wavefunction and is thus immaterial for spin-Hamiltonian parameter computations at given geometry (12). The double-ζ EPR-II (13) basis set was used for C and H atoms, as it has proven its efficiency for calculations of 1 H hyperfine couplings for Ti(III) alkyls (14,15). The cc-pCVTZ (triple-ζ with core correlation) basis set ( 16) was used for Al atom, following the previous calculations of 27 Al hyperfine and quadrupole couplings in Ti(III)-Al bimetallic system (17). The triple-ζ TZVP (18) basis set was used for all other atoms. For all calculations, the CPCM solvation model (19) was used.

Determination of π character in Ti-C bonds

The degree of π character in Ti-C bonds was estimated through the angle of deviation of the maximum amplitudes of the related natural hybrid orbitals (NHO) on α-carbon atoms of (CH2 t Bu) ligands from the Ti-C axis. The NHO were evaluated within the natural bond orbital (NBO) S8 analysis, performed in ORCA 4 (11) and GenNBO 7.0 (20) programs for the optimized structures. For the NBO analysis, the same DFT parameters as the ones used for the calculation of hyperfine and quadrupole tensors were applied.

detect it in all the samples (both boron-free and boron-containing) studied in the present work (see Figs. 2c &3a), which further supports our assignment of this signal to the spectroscopic signature of the active species.

HYSCORE spectroscopy.

The experimental 9.5 GHz HYSCORE spectrum (Fig. S7,a) contains the signals that originate from 1 H hyperfine and 27 Al hyperfine and quadrupole couplings. We do not find 35 Cl or 37 Cl signals, which might be expected in such system. Most likely, this is because of a distribution of the intensity over a broad range of frequencies due to strong quadrupole couplings. The intensity would then be below the noise level. The experimental HYSCORE spectrum was simulated using manual fitting by visual comparison of experimental and simulated HYSCORE spectral patterns (Fig. S7,b). The estimated parameters of 1 H hyperfine and 27 Al hyperfine and quadrupole couplings, together with the uncertainties of such an evaluation, are indicated below.

aiso Note that the manual simulation was performed assuming that there is only one coupled 1 H nucleus in the system. Therefore, the indicated Ti-H distance rTi-H = 3.16 ± 0.24 Å is an average distance to the most strongly coupled 1 H nuclei.

Evaluation of the structure of the active species.

Based on comparison of the experimental 9.5 GHz HYSCORE spectrum with simulations based on DFT-computed EPR parameters, a model for the structure of the active species of MgCl2supported Ziegler-Natta catalysts can be developed. In order to find a reasonable model, several likely structures were designed and optimized using the DFT approach. Afterwards, the parameters of 1 H hyperfine and 27 Al hyperfine and quadrupole couplings were calculated for each structure and used for simulations of HYSCORE spectra. The setup of DFT calculations for geometry optimization and calculation of EPR parameters (see Materials and Methods) is based on experience in such calculations for Ti(III) alkyl complexes (14,15). Note that the 27 Al hyperfine couplings in Ti(III)-Al complexes may not be a reliable indicator of agreement between model and experimental data, as, with the used basis set, the magnitude of 27 Al hyperfine coupling was underestimated for Ti(III)-Al bimetallic complex (17). However, the 1 H hyperfine couplings were previously found to provide fingerprints of different conformations of Ti(III) alkyls, being rather sensitive to even small structural changes (14). Therefore, we used similarity of simulated and experimental 1 H HYSCORE patterns as the main criterion for evaluation of the right model ("precise criterion"). We still rejected models for which the calculated 27 Al hyperfine couplings aiso ( 27 Al) were larger by more than 1 MHz compared to the experimental values, estimated by the manual fitting ("less precise criteria"), or where the 27 Al hyperfine couplings are negligibly small (see Fig. S8,c).

S11

Using such an approach, we were able to reject all structures where the Ti(III) center does not have directly coordinated alkyl ligands (Fig. S8, a &b), since the calculated aiso ( 1 H) values are too small in case of alkyl ligands being coordinated exclusively to an Al atom (Fig. S8,a). The same is true if the alkyl ligands are replaced by alkoxides -O(CH2)4Cl (Fig. S8,b) that could have been obtained via the ring opening of surface THF molecules (24). This indicates that reacted THF is not directly involved in the structure of the active center. Furthermore, only a very small aiso ( 27 Al) = 0.0016 MHz, which is in contradiction to experimental observations, is found in computations if the Al atom is not connected to Ti(III) through only one bridging atom (Fig. S8,c). Therefore, we assign the structure of the active species of Ziegler-Natta catalysts to a bimetallic Ti-Al complex with the organometallic ligands directly coordinated to Ti, as it was previously proposed by Guilio Natta (25).

At first, we tested models that contain a four-coordinated Al atom in their structure, including the (C2H5)ClTi-(μ2-Cl2)-Al(C2H5)2 model (Fig. S9,a) and the Cl2Ti-((C2H5)-Al(C2H5)2 model, where the α-C atom of (C2H5) ligand is coordinated to both Ti and Al atoms (Fig. S9,b). Both the structures shown on Fig. S9,a & Fig. S9,b did not provide sets of 1 H hyperfine couplings in agreement with the experimental HYSCORE spectrum and were therefore rejected. The same was true for the Cl2Ti-(C2H4)-Al(C2H5)2 model with X2-type C2H4 (Fig. S10,a), where a strong Ti-H interaction with the (C2H5) ligands coordinated to Al atom results in too large 1 H hyperfine couplings that are not observed in the experimental spectrum. Therefore, we examined as well the structure with a substitution of Al(C2H5)2Cl units by AlCl2(C2H5), namely the Cl2Ti-(C2H4)-AlCl(C2H5) model (Fig. S10,b). For this model, the simulated 1 H HYSCORE pattern is reasonably close to the experimental one. However, for this species we compute 27 Al isotropic hyperfine coupling of aiso( 27 Al) = -4.67 MHz, being too large in magnitude compared to the experimental value of 1.5 MHz. Note that DFT computations on our basis set level without diffuse functions typically underestimate the isotropic 27 Al hyperfine coupling. Among all models considered, only the one shown in Fig. 3c and Fig. S11, a, provides a set of 1 H hyperfine couplings that fits the HYSCORE spectrum nearly perfectly (see Fig. 3d & Fig. S11, b -f). This model also provides 27 Al hyperfine and quadrupole couplings (see Table S4), which are close to the ones derived from the manual fit. For instance, the calculated dipolar part of the hyperfine tensor adip (calculated) = [-0.36 -0.49 0.85] MHz is in a nice agreement with the one derived from the manual fit, adip (experimental) = [-0.5 -0.5 1.0] MHz. Likewise, the calculated quadrupole coupling constant P (calculated) = -44.71 MHz is also close to P (experimental) = -42 MHz, derived from the manual fit. These observations indicate that the Ti-Al distance and the symmetry of the coordination sphere of the nearby Al atom are predicted well by the model shown on Fig. 3c and Fig. S11,a. The calculated 27 Al isotropic hyperfine coupling aiso (calculated) = 0.31 MHz is significantly smaller than aiso (experimental) = 1.5 MHz. However, such an underestimate is consistent with previous observations for the used basis set (17). Therefore, we conclude that this model for the active species of heterogeneous Ziegler-Natta catalysts is consistent with our experimental data. g tensor calculations.

The unexpectedly large experimental g values are reminiscent to an observation by Skorobogaty et al. for the 3,10,17,24-tetrasulphonatophthalocyanin (tspc) complex of V(IV)O in a water/DMF solution after two-electron reduction of the macrocycle (26). These authors also observed shifts in the g values when substituting DMF by pyridine, with g|| still being unusually large for a V(IV)O species, and a change to g values common for V(IV)O and g^ > g|| when the S12 complex was adsorbed on a Sephadex column. For V(IV)O(tspc), assignment to a 3d 1 vanadyl species is safe, since the 57 V hyperfine couplings prove large spin density on vanadium and are typical for V(IV)O species, whereas 3d 3 species have different EPR properties.

We were accidentally able to reproduce the unusually high g values of 3d 1 species V(IV)O(tspc) by DFT, using the BP86 functional (27) together with the def2-TZVPP (28) basis set for all atoms and the CPCM (19) solvation model for water; the computations were performed in ORCA 4 with KDIIS and the switching to the second-order SCF (SOSCF) convergence procedure [START_REF] Neese | Approximate second-order SCF convergence for spin unrestricted wavefunctions[END_REF]. The principal values of the calculated g tensor for V(IV)O(tspc) of g = [2.0081 2.0094 2.1316] appeared surprisingly close to the experimental g values for the active species of Ziegler-Natta heterogeneous catalysts. However, even for V(IV)O(tspc), where these unusually high g values are experimentally observed, we were not able to reproduce them with the PBE0 (5) and TPSSh [START_REF] Perdew | Meta-generalized gradient approximation: Explanation of a realistic nonempirical density functional[END_REF] functionals, where we obtained g = [1.9573 1.9720 2.0065] and g = [1.9781 1.9891 1.9899], respectively. In fact, a computation with DIIS and without the SOSCF convergence procedure even revealed a lower-lying minimum for the BP86 function that corresponded to g values lower than 2, as they are not observed for this species. Given this large variability among DFT results for the g tensor, we do not consider these computations as reliable for this type of electronic structure.

The π character of the Ti-C bond.

The structure, shown in Fig. 3c, is notable for a weak p character in the Ti-C bond, which is evidenced by the calculated deviation of the natural hybrid orbital (NHO) on carbon from the Ti-C axis [START_REF] Gordon | NMR chemical shift analysis decodes olefin oligo-and polymerization activity of d0 group 4 metal complexes[END_REF]. For this structure such a deviation is equal to ΘNHO-C-Ti = 9.0°. This makes the proposed structure similar to previously described β-diiminato Ti(III) alkyls, which are active towards ethylene polymerization going via a so-called "augmented" Cossee-Arlman mechanism (14). Such a mechanism involves a combination of the π character in metal-carbon bonds and the delocalization of the unpaired electron, which together favor the ethylene insertion into the metalcarbon bond. Both these factors are present in the evaluated structure (Fig. 3c & Fig. S11,a), which makes it a potentially highly active catalyst of ethylene polymerization. This further supports our assignment of the structure, shown on Fig. 3c, to the active center of MgCl2-supported Ziegler-Natta catalysts.

S14

Figure S2

Solid-state 11 B MAS NMR spectrum of heterogeneous Ziegler-Natta catalyst (B/Ti = 1.35) after Et3Al addition. Signals "1" correspond to boron alkoxides as it is showed in Fig. S2, a. Signal "2" at 85 ppm correspond to chemical shift of triethylboron. Narrow and symmetric line shape of signal "2" without quadrupolar broadening indicates that triethylboron is present in liquid state with allowed free-rotation. S3.

S24

Table S1.

Elemental analysis of all prepared samples of heterogeneous Ziegler-Natta catalysts, together with their activities towards ethylene polymerization and the molar masses of produced polyethylene. When no H2 was used in combination with ethylene (to control molar masses), an even higher activity was monitored as previously observed [START_REF] Humbert | Activity enhancement of MgCl2-supported Ziegler-Natta catalysts by Lewis-Acid pre-treatment for ethylene polymerization[END_REF].

B/Ti

Figure 1 .Figure 2 .

 12 Figure 1. a) Possible active centers A-C of MgCl2-supported Ziegler-Natta ethylene polymerization catalysts. b) Preparation of MgCl2-supported Ziegler-Natta catalysts for ethylene polymerization as studied in the present work.

Figure 3 .

 3 Figure 3. a) 9.5 GHz CW (black) and echo-detected (red) EPR spectra of Ziegler-Natta catalyst with B/Ti = 3. b) Experimental 9.5 GHz HYSCORE spectrum of Ziegler-Natta catalyst with B/Ti = 3, τ = [128 160 192] ns (blue to yellow), together with its simulation (red). c) Proposed structure of the active center of heterogeneous Ziegler-Natta catalysts (see also Fig. S11, a). d) Experimental HYSCORE spectrum of Ziegler-Natta catalyst with B/Ti = 3 (blue) and a simulation of experimental 1 H hyperfine couplings (green), based on DFT calculations on the proposed structure (see text & Fig. S11, b -f). The experimental HYSCORE spectrum (see Fig. S7) was measured at the magnetic field position indicated by an arrow in a).

  ( 1 H) = -7.5 ± 0.5 MHz Tdip ( 1 H) = 2.5 ± 0.5 MHz adip ( 1 H) = [-2.5 ± 0.5; -2.5 ± 0.5; 5.0 ± 1.0] MHz rTi-H = 3.16 ± 0.24 Å aiso ( 27 Al) = 1.5 ± 0.2 MHz adip ( 27 Al) = [-0.5 ± 0.1; -0.5 ± 0.1; 1.0 ± 0.2] MHz P ( 27 Al) = -42 ± 1 MHz η ( 27 Al) = 0.5 ± 0.1 MHz

Figure S1

 S1 Figure S1 Solid-state 11 B NMR spectroscopy study of heterogeneous Ziegler-Natta catalyst (B/Ti = 1.35) before Et3Al addition. 11 B NMR spectra (a) and the result of 2D MQMAS 11 B-11 B (b) showing that 11 B species are between 14 ppm and 28 ppm, typical chemical shifts of boron-alkoxydes.

2 S15Figure S3. Paramagnetic 1 H 7 1 S17Figure S5 .

 2171S5 Figure S3. Paramagnetic 1 H NMR spectrum of heterogeneous Ziegler-Natta catalysts in the presence of Et3Al (Al/Ti = 10) with a magnification in the -20/-100 ppm region on the paramagnetic contribution. a) B/Ti = 0; b) B/Ti = 1.35. Signals "1" correspond to the diamagnetic contribution in both spectra.

  Figure S6. a) EPR inversion recovery measurements for the samples of Ziegler-Natta catalysts with B/Ti = 0 (blue), B/Ti = 2.6 (black) and B/Ti = 4.1 (red). b) EPR Hahn echo decay measurements for the samples of Ziegler-Natta catalysts with B/Ti = 0 (blue), B/Ti = 2.6 (black) and B/Ti = 4.1 (red). T2 relaxation times were determined via monoexponential fitting of Hahn echo decay curves.

  Figure S11. a) Proposed structure of the active center of heterogeneous Ziegler-Natta catalysts. b) -d) Simulations of the 1 H HYSCORE pattern, based on the calculated 1 H hyperfine couplings for the three β-H atoms of the (C2H5) ligand of the Ti atom of the proposed structure of the active center. e) -f) Simulations of the 1 H HYSCORE pattern, based on the calculated 1 H hyperfine couplings for the two α-H atoms of the (C2H5) ligand of the Ti atom of the proposed structure of the active center.

gcat . h) -1 Activity, kgPE . (molTi . h) -1

  Since the[Ti] content of the sample of B/Ti = 3.00 was slightly higher than that of B/Ti = 4.10, it explains that it displayed the highest activity by weight of all tested catalysts and thus the highest corresponding characteristic signal intensity in EPR spectroscopy. Calculated over 1 min, instead of over the period of time necessary to achieve a 5-bar consumption of ethylene from the 2 L ballast that feeds the reactor, corresponding to about[START_REF] Perdew | Meta-generalized gradient approximation: Explanation of a realistic nonempirical density functional[END_REF] g of polyethylene, which we consistently verified gravimetrically (see Materials and Methods, Synthesis of Polyethylenes & Validation of Spectroscopic Methodology). In this experiment, realized with leftovers from an NMR sample preparation (~5 mg) without any external Et3Al added, a quick deactivation was observed, due to incomplete scavenging of the polymerization medium. Only 25 mg were used from a typical >40-mg EPR sample to avoid extreme exotherms in our 1L reactor with our typical procedure. No fast-deactivation profile was observed, probably due to the increased mass, representative of EPR samples (see Materials and Methods, Synthesis of Polyethylenes & Validation of Spectroscopic Methodology).

	Table S2-a.
	Activity tests in polyethylene synthesis of alkylated NMR samples
		Catalyst	Catalyst Powder Mass	Cocatalyst (overall Al/Ti)	Feed		Activity, kgPE . (gcat . h) -1	Activity, kgPE . (molTi . h) -1
		1			5 mg	Standard	1 bar H2/6 bar	8	9000
			ratio	%wt.	%wt. Conditions Polym.	Activity, ethylene		Activity,	* Mw,	* Đ = Mw/Mn
		(molar)		B	Ti (300)	kgPE . (gcat . h) -1	kgPE . (molTi . h) -1	kg . mol -1
	1	1	0		0 5 mg	4.48 added (10) None	12 ethylene 1 bar H2/6 bar	13 000 10 (1 min)* 220	3.8 11000 (1 min)*
	2 3 4 5 * Table S2-b. 1.35 2.60 3.00 4.10		1.1 1.12 1.54 1.66	3.7 1.9 2.27 ** 1.81	35 14 38 31		45 000 35 000 80 000 82 000	219 231 178 234	3.5 3.4 3.0 2.9
	Activity tests in polyethylene synthesis of alkylated EPR samples
		Catalyst kgPE . (1 Catalyst Powder Mass Cocatalyst Feed Activity, (overall Al/Ti) 10 mg Standard 1 bar H2/6 bar 5	5000
						Polym.	ethylene
						Conditions
						(300)
		5			5 mg	Standard	1 bar H2/6 bar	7	15000
						Polym.	ethylene
						Conditions
						(300)
		5			25 mg*	None	1 bar H2/6 bar	14	39000
						added (7)	ethylene
		5			25 mg*	None	7 bar ethylene	35	93000
						added (7)
	*
									S25

*Molar masses and Dispersities are determined by SEC-HT using universal calibration (see Materials and Methods, SEC).

**

Acknowledgments:

We acknowledge Olivier Boyron, Manel Taam and Edgar Espinosa (Université de Lyon) for SEC analyses and Rene Tschaggelar (ETH Zürich) for the technical support in EPR experiments. Funding: A.A. is supported by a SNF-ANR grant (Mr. . M.H. is supported by a ANR-SNF grant (MRCAT ANR-15-CE29-0025) Author contributions: A.A. and M.H. declare equal contribution. A.A. performed EPR spectroscopy and DFT computations. M.H. carried out the synthesis of the catalysts and evaluated the polymerization activity studies. J. B and M. H. performed NMR measurements. S.N., D.G., K.S and D.K. supervised the project. C.C., G.J., J. R., V.M., A.L. and G.P. coordinated the project and provided guidance. All authors participated in writing the final manuscript. Competing interests: Authors declare no competing interests. Data and materials availability: All data is available in the main text or the supplementary materials.

Supplementary Materials for

Lesage, G. Pintacuda, J. Raynaud, V. Monteil, C. Copéret, G. Jeschke

Correspondence to: jean.raynaud@univ-lyon1.fr; vincent.monteil@univ-lyon1.fr; ccoperet@inorg.chem.ethz.ch; gunnar.jeschke@phys.chem.ethz.ch

This file includes:

Materials and Methods Supplementary Text Figures S1 to S11

Tables S1 to S4 References measurements. For the home-built Q-band spectrometer, the magnetic field was corrected using Herasil (Heraeus) as a reference.

Correlation of activities and EPR signal intensities

In order to study the correlation between activities of the Ziegler-Natta catalysts and EPR signal intensities (Fig. 2a), the samples with different B/Ti ratios (B/Ti = 0; 2.60; 4.10) were synthesized as described before. The activities towards ethylene polymerization were measured as described before to be 12 kgPE . (gcat . h) -1 ; 14 kgPE . (gcat . h) -1 ; 31 kgPE . (gcat . h) -1 , respectively. All the samples were prepared in the quartz EPR tubes with a diameter 2.93 ± 0.04 mm. The solid powder of the catalysts was put into the EPR tubes to fill them to the same height of 2.4 ± 0.1 cm in order to fill completely the active zone of the EPR resonator. This corresponds to 43 ± 2 mg of the sample with B/Ti = 0, 49 ± 2 mg of the sample with B/Ti = 2.60 and 44 ± 2 mg of the sample with B/Ti = 4.10. The 1 M solution of Et3Al was added to each sample tube to the height of 3 cm on top of the solid powder (5.4 cm of total sample height); this grants its molar excess towards Ti (5-10:1).

All 9.5 GHz echo-detected EPR spectra (Fig. 2c) were measured at 10 K with a Bruker Elexsys E680 EPR spectrometer within one run of the spectrometer. A Bruker MS3 split-ring resonator with a length of the active zone of 1 cm was used for all the measurements. The sample tubes were placed in the resonator so that the bottom end of the tube was 0.8 cm lower than its active zone. All the measurements were performed in a similar broad microwave absorption mode of MS3 resonator with similar quality factor, which was achieved by the same height of resonator's antenna. The standard 2-pulse sequence π/2 -τ-π -τ-echo was used for all the samples, with τ = 144 ns, tπ/2 = 16 ns, tπ = 32 ns (tuned at the maxima of echo intensity using Rabi oscillations). The same repetition rate of 1 kHz was used to record all the echo-detected EPR spectra shown in Fig. 2c. We established the minimal safe repetition time for quantification by inversion recovery measurements (see Fig. S6,a). All other recording parameters, including the speed of the field sweep, the number of accumulations and the gain of the amplifiers, were the same for all the samples. We also checked that transverse relaxation times were the same within experimental uncertainty for all samples (T2 = 40 ± 3 ns, Fig. S6,b).

In Fig. 2a, all EPR signal intensities are normalized to the signal intensity for the sample with B/Ti = 4.10 by the ratio of weights of the samples. All EPR signal intensities were further normalized by the ratio of activity to intensity of the sample with B/Ti = 4.10.

HYSCORE spectroscopy

9.5 GHz HYSCORE measurements were performed on a Bruker Elexsys E680 EPR spectrometer using a Bruker MS3 split-ring resonator. For the HYSCORE measurements, the standard 4-pulse sequence π/2-τ-π/2-t1-π-t2-π/2-τ-echo with pulse lengths tπ/2 = 24 ns, tπ = 16 ns was used. Time steps Dt1 and Dt2 were set to 16 ns. An eight-step phase cycle was used to remove unwanted echo contributions. The repetition rate was set to 2 kHz. The length of HYSCORE traces was set to 2160 ns in both dimensions. The final spectrum (Fig. 3b) was obtained via the summation of the HYSCORE spectra with τ = 128 ns, τ = 160 ns and τ = 192 ns to compensate for blind spots.

Simulation of HYSCORE spectra

All numerical simulations of experimental HYSCORE spectra were performed in EasySpin (1) with the saffron function, which took as an input either manually estimated parameters of 1 H

Supplementary Text Validation of Spectroscopic Methodology: Polymerization performances of alkylated NMR & EPR samples

To ensure that our spectroscopic study was representative of the active centers (these "pre-active sites" become active sites in the presence of ethylene), we ran "control" polymerizations with the "activated ZN powder" without and with externally added Et3Al, and with the toluene-dispersed activated catalyst (see Table S2). Noticeably, even the raw "activated ZN powder" dispersed in heptane promoted the polymerization of ethylene without any other reagent than monomer, although with a quickly deactivating profile. The expected activity profile was then restored when dispersing the "activated ZN powder" in the 3 mmolar-[Et3Al] heptane solution, and adding ethylene. Similarly, the EPR-representative sample promoted the polymerization of ethylene upon dispersion in heptane. This set of experiments is consistent with our spectroscopic experimental procedures allowing for a good insight in the ZN catalysis' active centers.

Paramagnetic NMR studies

The 11 B NMR spectroscopy reveals the presence of boron alkoxide species in the Bcontaining Ziegler-Natta precatalysts before Et3Al addition (Fig. S1) and the formation of BEt3 after the Et3Al addition (Fig. S2).

Paramagnetic 1 H-NMR techniques under ultra-fast MAS conditions (21) were applied to the samples of heterogeneous Ziegler-Natta catalysts with B/Ti = 0 and B/Ti = 1.35, activated with Et3Al. Similar 1 H paramagnetic species were found for both samples with and without BCl3, while being noticeably more pronounced in the spectra of the BCl3-treated samples (Fig. S3). These signals, having a paramagnetic shielding contribution, indicates the presence of Ti(III) alkyl species.

GHz EPR studies

The 35.7 GHz echo-detected EPR spectra (Fig. S4, a) of Ziegler-Natta catalysts with B/Ti = 0; 2.6; 4.2 show an intense line at 1280 -1500 mT, related to the signals around 340 -390 mT in 9.5 GHz CW EPR spectra (Fig. 2b). This line was previously assigned to several TiCl3 species, using Q-band (34 GHz) HYSCORE spectroscopy (10). In the present work, we tested for a possible contribution of Ti(III) alkyl species at these field positions, but did not detect coupled 1 H nuclei by Q-band HYSCORE, Q-band 3-pulse ESEEM (22) and Q-band Davies ENDOR (23) techniques.

The 35.7 GHz echo-detected EPR spectra show also a signal around 1276 mT (Fig. S4, a, marked with arrow) which tends to grow with an increase in the B/Ti ratio. This 35.7 GHz EPR signal is essentially the same signal with g = [2.0023 2.0053 2.1289], which was observed in 9.5 GHz pulse EPR spectra (Fig. 2c), as revealed by the comparison of the two spectra (Fig. S4,b). However, it is strongly suppressed in the 35.7 GHz echo-detected EPR spectra. Hence, it appears that only the 9.5 GHz CW EPR spectra (see Fig. 2b & Fig. 3a) contain the complete spectral pattern of paramagnetic species in heterogeneous Ziegler-Natta catalysts, showing two types of signals with g ≥ 2.0023 and g < 2.0023. While the former type is observed well in 9.5 GHz pulse EPR S9 spectra, but strongly suppressed in 35.7 GHz pulse EPR spectra, the latter type is easily observed in 35.7 GHz echo-detected EPR spectra, but completely suppressed at 9.5 GHz.

The signal with g = [2.0023 2.0053 2.1289] can also be recognized as a weak contribution in in Q-band echo-detected EPR spectra of boron-free Ziegler-Natta catalysts for propylene polymerization previously measured by others (10). This signal was not discussed in previous work, where it appeared to be a rather minor contribution. 9.5 GHz CW EPR studies All 9.5 GHz CW EPR spectra (Fig. 2b) were simulated assuming the presence of four distinct paramagnetic species, as described in the Materials and Methods part. While the g tensor parameters for the axially symmetric species (g = [2.0023 2.0053 2.1289]) were estimated from the simulation of the 9.5 GHz echo-detected EPR spectrum (Fig. S5,a) and kept fixed for all the other simulations, the EPR parameters for the other three paramagnetic species (Fig. S5, b -d) were relaxed during the fit. Despite the fact that all the spectra were fitted separately, the best-fit g tensor parameters are very similar across all samples for each of the species, with differences not exceeding line width (see Table S3). We thus evaluated the final g tensor parameters as averages of the individual fits. The species with gaverage = [1.8167 1.8657 1.9156] resembles the TiCl3 species on MgCl2 (110) surface with g = [1.84 1.888 1.936], described in previous work (10), while the species with gaverage = [1.8910 1.9402 1.9621] are close to previously characterized (10) TiCl3 species on MgCl2 (104) surface with g = [1.89 1.945 1.96]. The third of these species with gaverage = [1.9642 1.9716 1.9902] was also observed in previous characterization (g = [1.96 1.968 1.976]). We did not detect 1 H couplings for any of these species. Hence, we assign all of them to various surface species of TiCl3 type.

Correlation of activities and EPR signal intensities

All 9.5 GHz echo-detected EPR spectra, shown on Fig. 2c, were measured with the same filling height of the samples in the EPR tubes (see Materials and Methods) to ensure the same filling factor between the measurements. All the spectra were measured in the same broad mode of the Bruker MS3 resonator to ensure nearly the same quality factor between the measurements. Therefore, the EPR signal intensity is expected to depend only on the quantity of paramagnetic species.

We further studied the relaxation properties of the intense axially symmetric EPR signal, observed in 9.5 GHz pulse EPR spectra (Fig. 2c). The inversion recovery curves for all three samples with B/Ti = 0, B/Ti = 2.6 and B/Ti = 4.1 are shown on Fig. S6,a. Complete recovery of echo intensity is safely achieved in 500 μs after the π pulse. Hence, a repetition rate of 2 kHz or lower is safe. For quantification of relative spin count, the repetition rate was lowered to 1 kHz. The transverse relaxation time T2 = 40 ± 3 ns is the same within experimental uncertainty for all samples (Fig. S6,b). We can thus assume that echo intensity at 9.5 GHz depends only on the quantity of this species.

Based on the correlation between the 9.5 GHz pulse EPR signal intensity and the activity of the Ziegler-Natta catalysts (Fig. 2a), which is expected to be proportional to the number of the active sites of the catalysts, we assign the axially symmetric signal in the 9.5 GHz echo-detected EPR spectra (Fig. 2c) to the active species of MgCl2-supported Ziegler-Natta catalysts. This signal, being observable as well in 35.7 GHz (Q-band) pulse EPR spectra (Fig. S4), was previously detected by others in boron-free Ziegler-Natta catalysts of propylene polymerization (10). We Table S3. Parameters of the simulation of experimental CW EPR spectra of heterogeneous Ziegler-Natta catalysts. The error bars indicated for g tensor parameters are the FWHM Gaussian parameters of inhomogeneous line broadening.

Species


domain: shs info

text:
Marie Saade

Maroun Jneid
email: maroun.jneid@ua.edu.

Imad Saleh
email: imad.saleh@univ-paris8.fr

Maroun -0520-585x]

Jneid

Enhancing Decision-Making in New Product Development: Forecasting technologies revenues using a Multidimensional Neural Network

Keywords: Innovative product development, Forecasting product revenues, Neural Networks, Decision support

HAL is

Introduction

Nowadays, with the intention to increase financial revenues and to determine competitiveness in the market, organizations are constantly enhancing research and development-based digital innovation activities which represent a main driver for new product/service development [START_REF] Distanont | The role of innovation in creating a competitive advantage[END_REF]. For instance, Jneid and Saleh stated in their study that innovation represents the main component that contributes to the success of new start-ups encountering a competitive environment [START_REF] Jneid | Improving start-ups competitiveness and innovation performance: the case of Lebanon[END_REF]. However, despite the fact that organizations are constantly developing new products while increasing R&D investments and deploying new technologies, innovative trends are prone to failure, leading to undesired repercussions [START_REF] Christensen | The innovator's dilemma: when new technologies cause great firms to fail[END_REF]. Furthermore, when evaluating a product lifecycle, the decision-makers of the organization first need to consider and estimate the potential disruptive innovation, in order to assess when a product is threatened to enter the decline phase [START_REF] Sun | Achieving disruptive innovationforecasting potential technologies based upon technical system evolution by TRIZ[END_REF]. For example, Apple with its innovative iPhone series and Samsung with its Android operating system forced Nokia phones to enter the decline phase of their life-cycle in 2007 [START_REF] Sheffi | The power of resilience: How the best companies manage the unexpected[END_REF]. Nevertheless, while innovation disruptiveness can threaten the existing product market performance, the adoption of this disruptive technology may improve and lead to a new product development [START_REF] Obal | An Investigation on the Role of Disruptive Technology Adoption on New Product Market Performance and Launch Timeliness[END_REF].

The main problematic of the current study is to explore how disruptive innovation can be estimated quantitatively in an early stage, and therefore to examine how digital innovation development can be enhanced by forecasting new technology-based product success by applying a convenient predictive technique that can support the decision-making in the organizations.

Several previous studies proposed different methodologies to identify a potential disruptive innovation in order to improve efficiency during new product development process. Nagy, Schuessler and Dubinsky [START_REF] Nagy | Defining and identifying disruptive innovations[END_REF] suggested a qualitative study that can determine if a new candidate technology would be a potential disruptive innovation. This method highlights the importance of comparing the technical standards, the functionality and the ownership of this new candidate innovation with the existing technology currently used in the organization. In addition, Momeni and Rost [START_REF] Momeni | Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling[END_REF] proposed a systematic tool that can analyze quantitatively the relationship between technological disruptiveness and highly cited patents. It is based on patentdevelopment path, topic-modeling used for patent-citation analysis and k-core analysis to classify different subgroups of each technology. A quantitative study of a visualization bibliometric analysis has been elaborated as well to explore potential disruptive innovation [START_REF] Shang | A historical review and bibliometric analysis of disruptive innovation[END_REF]. However, each of those methodologies has limitations. For instance, the aforementioned qualitative procedure often rely on subjective judgments of decisionmaking experts, which may influence results [START_REF] Shang | A historical review and bibliometric analysis of disruptive innovation[END_REF]. [START_REF] Saade | Predicting technology success based on patent data, using a wide and deep neural network and a recurrent neural network[END_REF]. Publications data can be used as well to measure technological and scientific capabilities [START_REF] Geum | Identifying and evaluating strategic partners for collaborative R&D: Index-based approach using patents and publications[END_REF]. Furthermore, patents and publications data are considered among the most important dimensions that have potential benefits and reflection into the knowledge-based economy [START_REF] Han | Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology[END_REF][START_REF] Wisła | Patent Data in Economic Analysis[END_REF].

Patent and Publication-based technology forecasting methods:

Growth curves, scenario planning and analogies technology forecasting tools have been used based on patent and bibliometrics analysis, while focusing on food safety, fuel cell and optical storage technologies [START_REF] Daim | Forecasting Emerging Technologies: Use of Bibliometrics and Patent Analysis[END_REF]. Moreover, patent analysis, bibliometric analysis and the technology roadmapping method have been combined to visualize and predict the future development of the Nanogenerator technology in China [START_REF] Wang | Emerging nanogenerator technology in China: A review and forecast using integrating bibliometrics, patent analysis and technology roadmapping methods[END_REF]. Revenue forecasting studies: Different revenue forecasting techniques are used to evaluate the product level and thus to support planning and decision making. An efficient solution based on three different machine learning models for regression and time series forecasting has been provided to predict revenues and applied on Microsoft's Finance organization real finance data [START_REF] Gajewar | Revenue Forecasting for Enterprise Products[END_REF]. The travel toll revenue forecasting has been applied and illustrated in a probability distribution, using a Neural Network model [START_REF] Zhao | Evaluating Toll Revenue Uncertainty Using Neural Network Models[END_REF]. Moreover, an analysis on historical data has been conducted as well using a Neural Network model, in order to predict rental income for enterprises having several malls [START_REF] Sanjaya | Revenue Prediction Using Artificial Neural Network[END_REF].

However, although several previous studies propose to identify future technology development, none of them rely on combining patents, publications and technologies revenues as key performance indicators. Accordingly, and based on our research, the current study has not been applied so far when evaluating and monitoring technologybased product development in this specific context.

technology, using two separate types of Neural Networks for comparison purposes: a Wide and Deep Neural Network model and a Recurrent Neural Network model. These neural networks are to be implemented as following: The one-dimensional WDNN (ODWDNN) and RNN (ODRNN) models are employed to separately predict the patents and publications growth. Therefore, their predicted results, as well as the historical data of these two dimensions, will then be used as inputs in either a Multidimensional Wide and Deep Neural Network (MDWDNN) classifier for WDNN, or a Multidimensional Recurrent Neural Network (MDRNN) classifier for RNN. These multidimensional neural networks are designed to forecast future revenues ranges for specific trending technologies, and therefore evaluating and measuring technology-based product power.

Why use a Neural Network model: Based on different studies, Artificial Neural

Network models can outperform traditional models, such as Linear Regression [START_REF] Veerachai Gosasang | A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port[END_REF] and Autoregressive Integrated Moving Average [START_REF] Adebiyi | Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction[END_REF] in the context of time series forecasting and obtaining higher accuracy. In addition, since this study model will eventually be targeting organizations in a future work, a predictive neural network can be trained and tested through historical Big Data for an unsupervised [START_REF] Schmidhuber | Deep Learning in Neural Networks: An Overview[END_REF] and dynamic learning [START_REF] Alsheikh | Machine learning in wireless sensor networks:Algorithms, strategies, and applications[END_REF].

Why use a Wide and Deep Neural Network model:

A wide and deep model can handle complex large-scale data, integrating heterogeneous input data [START_REF] Elaraby | Deep Learning: Effective Tool for Big Data Analytics[END_REF]. Furthermore, Deep Learning algorithms can generalize the relationships in the data and the extracted representation, by extracting rare or new combinations by transitivity of correlations. However, deep neural network may over-generalize and extract less relevant features [START_REF] Cheng | Wide & Deep Learning for Recommender Systems[END_REF]. Accordingly, the exception rules and the memorization of features correlations or interactions in the historical data, is a crucial need to improve the neural network forecasting. Hence the power of combining a deep model for learning interactions within historical data and then generalizing the output on new data with a wide model for memorizing specific rules and learning exceptions [START_REF] Cheng | Wide & Deep Learning for Recommender Systems[END_REF].

Why use a Recurrent Neural Network model:

A recurrent neural network can process sequences of inputs, which represent the time series of patents and publications in our case, with different lengths, to return a sequence of outputs [START_REF]Handbook of medical image computing and computer assisted intervention[END_REF]. In addition, LSTM (Long Short-Term Memory) can be used in the RNN to reduce the vanishing gradient problem that occurs in the basic recurrent neural networks [START_REF] Goyal | Deep learning for natural language processing. Deep Learning for Natural Language Processing: Creating Neural Networks with Python[END_REF].

Proposed Methodology

The current methodology relies on three dimensions: patents, publications and revenues. It first predicts the growth of the number of patents and the number of publications respectively, which will be used in the final prediction phase of revenues ranges. It covers different principle objectives explained in Table 1. Noting that these objectives and tasks are applied for both neural network types.

Table 1. Methodology design objectives.

Objective: Data Collection 1

Listing new and old trending technologies based on different sources.

2

Searching for related keywords for each technology.

Objective: Database integration 3

Inserting the collected data into a new integrated database.

Objective: Data Collection 4

Extracting patents, publications and revenues data for each technology from several sources based on keywords matching.

Objective: Database integration Manipulating and inserting the collected data into the integrated database.

Objective: Data manipulation Computing the total number of patents / publications per technology per year.

Objective: Datasets creation

Objective: Results visualization and Decision-making

Illustrating the revenues variation in statistical graphs. Evaluating the candidate technology based on business perspective.

Neural Network Structures

The present Neural Networks are implemented using the TensorFlow1 software library under Python. As previously mentioned, two Neural Network types are structured as following:

Neural Network 1. The first neural network is implemented using two different models for comparison purposes, in order to separately estimate the number of patents and the number of publications.

One-dimensional WDNN (ODWDNN). This Neural Network is structured as a "DNN Linear Combined Regressor" where the predicted output represents a continuous variable. It is designed with the same architecture and configuration as the WDNN of the Saade, Jneid & Saleh paper [START_REF] Saade | Predicting technology success based on patent data, using a wide and deep neural network and a recurrent neural network[END_REF]: ODWDNN contains h hidden layers, and each layer contains a specific number no of nodes. It is built based on the Adam optimization method which is designed for training deep neural networks and can outperform other stochastic optimization methods [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]. In addition, the number of patents / publications will be predicted for the technology in question for each future year separately, where each predicted output is serving as input for the next prediction until the number of future years p is reached.

One-dimensional RNN (ODRNN)

. This Neural Network model is structured with the same architecture and configuration as the RNN in the study of Saade, Jneid & Saleh [START_REF] Saade | Predicting technology success based on patent data, using a wide and deep neural network and a recurrent neural network[END_REF]: ODRNN contains h recurrent hidden layers, with a specific number no of nodes.

It is built based on the following features:

-Long Short-Term Memory (LSTM) cells: they are activation cells that can outperform traditional recurrent layers. -Sequence-to-Sequence (seq2seq) neuronal architecture: where the RNN encodes a variable-length sequence of inputs into a fixed-length vector representation and then decoding a fixed-length vector representation into a sequence of outputs [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF]. Noting that the input sequences lengths vary according to the data availability of each technology. -Many-to-many structure: where both the input and the output represent sequences of data with same or different lengths. -Adam optimizer: can outperform other stochastic optimization methods [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]. In addition, the output layer represents a sequence of number of patents / publications forecasted in a single step from Year1 till Yearp.

Neural Network 2.

The second Neural Network is implemented using two different models for comparison purposes. They are designed as multidimensional neural networks and classification predictive models. Patents and publications data represent the inputs dimensions and the ranges of revenues represent the output classes. This classifier would return a probability distribution for each of the defined classes.

Multi-dimensional WDNN (MDWDNN).

MDWDNN is structured as a "DNN Linear Combined Classifier". Adam is applied as well in the MDWDNN as an optimization method. This Neural Network consists of the following layers: • Input Layer: The input layer represents the number of patents and publications for a technology for each available year.

• Hidden Layers: This neural network contains h hidden layers, and each layer contains no nodes.

• Output layer: represents the revenues ranges to be forecasted. Noting that each predicted output is serving as input for the next prediction until the number of future years p is reached.

Multi-dimensional RNN (MDRNN).

MDRNN processes time series sequences of multidimensional inputs, and predicts the probability distribution for each class for these sequences. In addition, it is based on encoder-decoder sub-models using LSTM and is structured as a Many-to-One model for classification usage, where the inputs represent sequences of data and the output represents a category. Moreover, Adam is applied as well in the MDRNN as an optimization method. Accordingly, it is built based on the following three layers:

Fig. 2. Multi-dimensional Recurrent Neural Network (MDRNN).

 Input Layers: MDRNN consists of a specific number nb of input layers or dimensions. They take time series sequences with a variable length.  Hidden Layers: Since a multidimensional recurrent neural network (MDRNN) consists of replacing the single self-connection in each node in a standard recurrent neural network with as many of self-connections as there are dimensions in the input data [START_REF] Graves | Multidimensional Recurrent Neural Networks[END_REF], MDRNN contains h recurrent hidden layers, with a specific number no of nodes, where each node in each hidden layer contains nb self-connections.  Output Layer: The output layer represents a sequence of classes to be predicted in a single step from Year1 till Yearp.

Experimentation

In order to train and test the neural networks, patents, publications and revenues data are collected for 15 trending technologies. Noting that since this study model will be targeting actual organizations in a future work, this predictive neural network can be eventually trained and tested using historical Big Data. The candidate technology's data are applied on the neural networks to predict its future patents, publications and its revenues ranges for the next four years.

Data Collection

Technologies Listing. Different web sources such as IEEE, Scientific American, Elsevier, etc. are used to list several trending technologies and their related keywords, such as the "Additive manufacturing" term for the "3D printing" technology.

Patent Data Source. Data of the granted patents applications that have been published before December 31, 2019, are extracted from the United States Patent and Trademark Office (USPTO) database which is considered among the richest intellectual property databases [START_REF] Uspto | [END_REF].

Publications Data Source. Publications data such as title, year, author name, etc. are extracted from Springer that includes a wide range of bibliometric resources.

Revenue Data Source. The revenues related to each technology for several years have been extracted from different web sources, such as: Statista web pages, Smart Insights and different reports and articles.

Training, Testing and Prediction Datasets Creation

Given that the amount of the collected data is limited in volume, it has been split manually into training and testing datasets. Furthermore, in order to compare the predicted values with the actual values and to visualize the accuracy of the Neural Networks, the validation dataset is also taken as the prediction dataset, where "Virtual Reality" represents the candidate technology for which its future values will be forecasted. In addition, the predicted outputs of ODWDNN and ODRNN are integrated into MDWDNN and MDRNN respectively, along with the historical data of patents, publications and revenues dimensions. Furthermore, given that Neural Network 2 (MDWDNN and MDRNN) represents a classifier model that predicts the future revenues ranges for a specific trending technology, revenues data have been grouped into 3 classes based on data availability, as following:

Neural Networks Implementation

The configurations and the training parameters of the neural networks have been adjusted and tuned progressively and determined experimentally according to the most accurate results. In addition, in order to ensure a fair comparison, the number of training steps applied for both neural network types are similar, and equal to 1000 steps.

Neural Network 1. Regarding the results accuracy of the ODWDNN and ODRNN models, it is calculated based on the same steps and formulae employed in the Saade, Jneid & Saleh article [START_REF] Saade | Predicting technology success based on patent data, using a wide and deep neural network and a recurrent neural network[END_REF].

One-dimensional WDNN (ODWDNN).

ODWDNN is implemented with the following main parameters, as defined in the Proposed Methodology section:

One-dimensional RNN (ODRNN). It represents a Sequence-to-Sequence Recurrent Neural Network. It has been built and implemented based on the following main parameters defined in the previous sections:

Neural Network 2. The results accuracy of the Multi-dimensional Neural Networks Classifiers MDWDNN and MDRNN are generated based on the built-in Softmax function that provides the probability distribution for output classes [START_REF] Tang | Document modeling with gated recurrent neural network for sentiment classification[END_REF], which means Softmax takes the predicted output values and normalizes them into probabilities.

Multi-dimensional WDNN (MDWDNN).

MDWDNN is implemented with the following main parameters, as defined in the Proposed Methodology section:

Multi-dimensional RNN (MDRNN). The parameters of MDRNN are configured as following:

Results

The following subsections represent the obtained results for the "Virtual reality" candidate technology using both types of Neural Networks, illustrating the predictions quality, where the ordinate axes represent the dimensions values and the abscissa axes represent the time.

Neural Network 1

As per the below graphs and Table 3, the ODRNN achieves a better performance and accuracy and outperforms the ODWDNN, given that the average prediction accuracy of the ODRNN is higher than that of the ODWDNN. One-dimensional RNN (ODRNN). Neural Network 2. As per the following table, the MDRNN achieves a better performance and accuracy and outperforms the MDWDNN, given that the MDRNN was able to detect the actual revenue class of the fourth year by 39.94%, whereas the fourth probability of the MDWDNN was 0%. Therefore, the proposed approach provides answers related to technology-based product success and reliable prediction models. The latter can be used by decisionmakers in the organizations to evaluate and monitor a new trending innovation, in order to either measure its impact on existing product market performance, or to adopt this potential disruptive technology that may lead to a new product development. Consequently, addressing this estimation is a crucial prerequisite step before proceeding with investments, where organizations can improve decision making in innovative technology-based product development. Furthermore, this method can be applied on several candidate technologies in order to prioritize and identify the most appropriate ones for a given investment project. This methodology can be further developed by addressing its current limitations. For instance, accessing Big data represents the most challenging task faced in the current study where patents data are based uniquely on USPTO patents applications and

  Grouping patents / publications data by technology for max years. max represents the maximum number of years per technology where historical data is available in the training dataset. Objective: Neural Network 1 (NN1) implementation Training the first Neural Network (ODWDNN and ODRNN) with patents data. Testing the Neural Network (ODWDNN and ODRNN) with patents data. Predicting the number of patents for the candidate technology for the next p years. Objective: Database integration Inserting the output data of Neural Network 1 (ODWDNN and ODRNN) into the integrated database. Objective: Neural Network 1 (NN1) implementation Training the first Neural Network (ODWDNN and ODRNN) with publications data. Testing the Neural Network (ODWDNN and ODRNN) with publications data. Predicting the number of publications for the candidate technology for the next p years. Objective: Database integration Inserting the output data of Neural Network 1 (ODWDNN and ODRNN) into the integrated database. Objective: Dataset creation Grouping patents, publications and revenues data by technology and by year. Objective: Neural Network 2 (NN2) implementation Training the second Neural Network (MDWDNN and MDRNN) based on the three dimensions. Testing the Neural Network (MDWDNN and MDRNN) with patents, publications and revenues data. Predicting the revenues classes for the candidate technology for future p years. Objective: Database integration Inserting the output data of Neural Network 2 (MDWDNN and MDRNN) into the integrated database.

Fig. 1 .

 1 Fig. 1. Multi-dimensional Wide and Deep Neural Network (MDWDNN).

p = 4

 4 years; max = 10 years; h = 5 hidden layers; no1 = 1000 nodes; no2 = 750 nodes; no3 = 500 nodes; no4 = 300 nodes; no5 = 150 nodes nb = 2; p = 4 years; h = 2 recurrent layers; no = 250 hidden nodes per layer p = 4 years; max = 10 years; h = 2 recurrent layers; no = 250 hidden nodes per layer p = 4 years; max = 10 years; h = 5 hidden layers; no1 = 1000 nodes; no2 = 750 nodes; no3 = 500 nodes; no4 = 300 nodes; no5 = 150 nodes One-dimensional WDNN (ODWDNN).

Fig. 3 .

 3 Fig. 3. Actual and predicted number of patents in the ODWDNN for the "Virtual reality" technology from 2006 to 2019.

Fig. 4 .

 4 Fig. 4. Actual and predicted number of publications in the ODWDNN for the "Virtual reality" technology from 2006 to 2019.

Fig. 5 .

 5 Fig. 5. Actual and predicted number of patents in the ODRNN for the "Virtual reality" technology from 2006 to 2019.

Fig. 6 .

 6 Fig. 6. Actual and predicted number of publications in the ODRNN for the "Virtual reality" technology from 2006 to 2019.

  The present study represents a proof of concept where it focuses on forecasting the patents, publications and revenues dimensions growth for a single candidate technology, noting that when this approach is to be applied on a real case study on organizational level, it can involve both a new technology representing a potential disruptive innovation, and an existing technology currently used in the organization,

	for comparison purposes.
	In addition, given the fact that a single product may embed several combined
	technologies (such as a drone that can be composed of 3D mapping technology,
	Infrared cameras, etc.), and since patents and publications data are technology-bound
	whereas revenues data are product-bound, the current study focuses on a specific type
	of product which is based on one major trending technology.
	The remainder of this paper is structured as following: Section 2 presents the related
	work on the patent, publication and revenues-based forecasting methods. Section 3
	presents the research design and the proposed methodology. The experimental setting
	of the present study and the results are described in section 4. Section 5 concludes the
	work and points to the study limitations and to future research directions.
	2	Literature Review
	R&D-based digital innovation activities represent a main driver for new
	product/service development. Patents data play a significant role in forecasting the
	success of trending technologies as justified in the paper "Predicting Technology
	Success based on Patent Data, using a Wide and Deep Neural Network and a
	Recurrent Neural Network"
		The patent-development path
	solution and the bibliometrics-based analysis cover only single indicators, patents and
	bibliometrics dimensions respectively.
	Consequently, to address this gap, the technologies-based products development
	process can be monitored by analyzing historical data of the related R&D activities
	and products revenues. Specifically, patents, publications and revenues are considered
	major key performance indicators and effective dimensions in measuring technology-
	based product power. In this regard, we propose a new methodology based on two
	main steps: forecasting patents and publications growths separately for a specific
	candidate technology using a common predictive Neural Network regression, then
	integrating the results into a Multi-dimensional Neural Network classifier in order to
	predict the future revenue-volume for this candidate technology. The present
	methodology is applied using two different types of Neural Network models for
	comparison purpose: "Wide and Deep Neural Networks" and "Recurrent Neural
	Networks".

Consequently, addressing this estimation represents a decision support in technologybased product/service development and a prerequisite step before proceeding with investments.

Table 2 .

 2 Revenues classes.

	Class Revenues ranges (in USD)
	1	0 <= Revenue <= 10 Billion
	2	10 Billion <= Revenue <= 50 Billion
	3	Revenue > 50 Billion

Table 3 .

 3 Prediction accuracies of Neural Networks 1.

	ODWDNN	ODRNN
	Patents prediction	Publications	Patents prediction	Publications
	accuracy	Prediction accuracy	accuracy	Prediction accuracy
	51.40%	83.58%	84.86%	70.18%
	Average prediction accuracy: 67.49%	Average prediction accuracy: 77.52%

Table 4 .

 4 Prediction accuracies of Neural Network 2.Research and Development-based digital innovation activities represent a main driver for new product development and an added value in today's business economy. However, despite the fact that organizations are constantly developing new products while increasing the R&D investments and deploying new technologies, innovative trends are prone to failure. In addition, when evaluating a product life-cycle, the decision-makers of the organization first need to consider the potential disruptive innovation. Accordingly, predicting technology-based product success is a crucial need and a prerequisite step before proceeding with investments. A new quantitative approach has been proposed in the current paper, based on patents, publications and revenues dimensions analysis, and using Neural Network models that can evaluate and monitor a candidate technology-based product development. These dimensions are considered major key performance indicators to measure the technology power. Precisely, this paper covers two main steps: forecasting patents and publications growths separately using a common predictive Neural Network model, then integrating the results into a Multi-dimensional Neural Network classifier model in order to predict the future revenue-volume which can reflect the technology-based product success. The present methodology is applied using two different types of Neural Network models for comparison purpose: Wide and Deep Neural Networks and Recurrent Neural Networks. These neural networks have been implemented using United States Patent and Trademark Office data, Springer publications data and several web sources in order to collect technologies revenues data. In addition, this method has been experimented on 15 trending technologies to train the neural networks, then applied on one additional candidate technology, "Virtual reality", for the testing and prediction phases. The findings show that the Recurrent Neural Networks models achieve higher average prediction accuracy, and outperform the Wide and Deep Neural Networks in both steps, proving to be a valid model that can enhance digital innovation development.

				MDWDNN		MDRNN
		Future	Actual	Predicted	Actual Class	Actual	Predicted	Actual Class
		years	Class	Class	Prediction	Class	Class	Prediction
					Accuracy			Accuracy
		Year 1 1	1	99.50%	1	1	100%
		Year 2 1	1	99.92%	1	1	99.99%
		Year 3 1	1	99.99%	1	1	99.99%
		Year 4 2	1	0%	2	1	39.94%
			Average prediction accuracy:	Average prediction accuracy:
			74.85%			84.98%
	5	Conclusion/Discussion

Research DesignThe analysis of patents, publications and revenues data is considered as a quantitative approach to assess their impact on the technology-based product success. Precisely, this study visualizes the historical growth of patents, publications and revenues dimensions, as well as their predictive future variation for a given candidate

Tensorflow is an open-source API created by Google and is used for Machine Learning purposes.

publications information are extracted only from Springer. Moreover, the proposed methodology can be further evolved to include products composed of multiple major technologies to support all types of technologies-based products.