Structural transformation and catalytic hydrogenation activity of amidinate-protected copper hydride clusters

Copper hydrides are important hydrogenation catalysts, but their poor stability hinders the practical applications. Ligand engineering is an effective strategy to tackle this issue. An amidinate ligand, N,N′-Di(5-trifluoromethyl-2-pyridyl)formamidinate (Tf-dpf) with four N-donors has been applied as a protecting agent in the synthesis of stable copper hydride clusters: Cu11H3(Tf-dpf)6(OAc)2 (Cu11) with three interfacial μ5-H and [Cu12H3(Tf-dpf)6(OAc)2]·OAc (Cu12) with three interstitial μ6-H. A solvent-triggered reversible interconversion between Cu11 and Cu12 has been observed thanks to the flexibility of Tf-dpf. Cu11 shows high activity in the reduction of 4-nitrophenol to 4-aminophenol, while Cu12 displays very low activity. Deuteration experiments prove that the type of hydride is the key in dictating the catalytic activity, for the interfacial μ5-H species in Cu11 are involved in the catalytic cycle whereas the interstitial μ6-H species in Cu12 are not. This work highlights the role of hydrides with regard to catalytic hydrogenation activity. Copper hydrides have been studied for their exciting structural chemistry and applications in hydrogenation catalysis. Here, the authors uncover the role of the amidinate ligand in yielding two closely related copper hydride clusters with quite different catalytic hydrogenation activity.

Surface organic ligands are critical in the construction and stabilization of atomically precise metal nanoclusters [20][21][22][23][24][25][26][27] , ligand engineering is an important approach in promoting the stability of copper hydrides. Envisioning multidentate amine ligands could provide stronger protection to metal clusters due to their multiple binding sites and their anionic nature which is helpful for ligating cationic metal ions [28][29][30][31][32] , we chose an amidinate ligand, N,N′-Di(5trifluoromethyl-2-pyridyl)formamidinate (Tf-dpf) containing four N-donors, as the protecting agent for copper hydride clusters. Such a strong protection of ligand shell favors the high stability of copper hydride clusters. Moreover, Tf-dpf has a flexible linear structure favoring the generation of metal cluster diversity 30 , which may be constructive in establishing structure-property relationships in terms of hydrogenation catalysis.
Herein, we report two amidinate-protected copper hydride clusters Cu 11 H 3 (Tf-dpf) 6 (OAc) 2 (Cu 11 ) and [Cu 12 H 3 (Tf-dpf) 6 (OAc) 2 ]·OAc (Cu 12 ), and their reversible interconversion (Fig. 1). The hydride positions in Cu 11 and Cu 12 were further confirmed by a machine-learning model based on convolutional neural networks (CNN) and trained on published structures of copper hydride clusters from neutron diffraction. It is quite unexpected that Cu 11 showed high activity in the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP), while Cu 12 displayed very low activity. Structural determination of these two clusters revealed that the type of hydride is the key in dictating the catalytic activity. Cu 11 has three interfacial μ 5 -H and Cu 12 has three interstitial μ 6 -H. Deuterated catalytic experiments confirmed that the μ 5 -H of Cu 11 is involved in the catalytic cycle whereas the μ 6 -H of Cu 12 is not active . These findings are not only helpful for understanding the catalytic mechanism, but also instructive for the design and synthesis of efficient hydrogenation catalysts.

Results
Synthesis and characterization. HTf-dpf ligand was synthesized by heating the mixture of 5-(trifluoromethyl)-2-aminopyridine and excess triethyl orthoformate (TEOF) at 120°C under nitrogen atmosphere (Supplementary Fig. 1) 33 . The preparation of Cu 12 involves the direct reduction of a mixture of Cu(OAc) and HTf-dpf/Et 3 N with a mild reducing agent, Ph 2 SiH 2 in a mixed CH 2 Cl 2 /CH 3 OH solvent. Cu 11 was obtained by changing the reaction solvent to CH 2 Cl 2 /DMSO (dimethylsulfoxide), and then crystallized from CH 2 Cl 2 and n-hexane. 1 Fig. 3b) in CD 3 OD show one singlet at −63.28 and −63.88 ppm, respectively (free HTf-dpf presents at −60.21 ppm, Supplementary Fig. 1b), suggesting that the six Tf-dpf ligands in Cu 11 and Cu 12 are in similar environments, respectively.
As shown in Fig. 2a, the positive ESI-MS spectrum of Cu 11 shows two prominent peaks, corresponding to the molecular ion [Cu 11 H 3 (Tf-dpf) 6  spectra of Cu 11 and Cu 12 in MeOH display three prominent absorption bands at 238, 288, and 340 nm, which are corresponding to the intraligand transitions of the Tf-dpf ligand, as similar bands are found in HTf-dpf ( Supplementary Fig. 4). To our surprise, Cu 11 and Cu 12 are very stable under ambient conditions. In the solid state, they are air and moisture stable ( Supplementary Fig. 5). In addition, Cu 11 and Cu 12 are stable in solution (even in polar solvents such as CH 2 Cl 2 ) for 2 weeks (Supplementary Fig. 6).

Molecular structures.
Single-crystal X-ray diffraction (SCXRD) structural analysis (Supplementary Table 1) revealed that Cu 11 comprises a Cu 11 H 3 (Tf-dpf) 6 (OAc) 2 cluster ( Fig. 3a and Supplementary Fig. 7), wherein six Tf-dpf ligands are ligated to Cu 11 (μ 5 -H) 3 core in a linear pattern (four in motif A and two in motif B) with Cu-N bond lengths ranging from 2.026(4) to 2.131(4) Å (Supplementary Table 2). Two OAc − anions bind the two copper atoms at the ends of the linear Cu 11 (μ 5 -H) 3 unit, giving the Cu−O bond lengths of 2.021(4) to 2.038(4) Å. The metal core of Cu 11 could be regarded as the fusion of three edge-sharing rectangular pyramids. The Cu…Cu distances of the Cu 11 skeleton range from 2.428(1) to 2.749(1) Å.
The structure of Cu 12 includes a [Cu 12 H 3 (Tf-dpf) 6 (OAc) 2 ] + cationic cluster (Fig. 3b) and a OAc − counter anion (Supplementary Fig. 8). The coordination modes of OAc − in Cu 12 are similar to that of Cu 11 , with the Cu-O bond lengths ranging from 2.111(5) to 2.120(4) Å. The six Tf-dpf ligands in Cu 12 adopt distorted motif A binding mode, with the Cu-N bond ranging from 2.000(5)-2.096(5) Å. The metal core of Cu 12 could be regarded as the fusion of three face-sharing octahedra. Moreover, the 12 copper atoms in Cu 12 are typical hexagonal close-packed type structure with ABAB packing mode ( Supplementary Fig. 9). Cu…Cu distances of Cu 12 skeleton range from 2.497(1)-2.764(1) Å, which is much longer than that of Cu 11 . Shorter Cu…Cu contact in Cu 11 could be attributed to the linear coordination mode of Tf-dpf, while Tf-dpf adopts zigzag coordination mode in Cu 12 to form relatively longer Cu…Cu contacts (average 2.657 Å) as shown in Fig. 3c. These Cu…Cu distances observed in Cu 12 is comparable to the average Cu…Cu contact of 2.66 Å in the Cu 6 octahedral structures 38 .
Neural network prediction of hydride sites. Even though the location of H atoms by SCXRD is difficult, the hydrides in Cu 11 and Cu 12 could be estimated based on the charge distribution in their cluster frameworks and refined freely. Although attempts to grow single crystals suitable for neutron diffraction were unsuccessful, we applied a recently developed machine-learning model based on CNN to confirm the hydride location. The CNN method can quickly predict hydride occupancy in a Cu cluster given the heavy-atom coordinates 39,40 . We fed the SCXRDdetermined positions of heavy-atoms into the CNN model and predicted the most probable sites in the two clusters. As shown in Fig. 4, the CNN model predicted close-to-1 occupancies in three sites for both the Cu 11 and Cu 12 clusters. The locations of these top three sites are shown in Fig. 4 insets; indeed, they exactly match the sites determined from SCXRD. Further density functional theory (DFT) geometry optimizations confirmed the stability of these clusters, as the cluster framework was well maintained after structural relaxation with hydrides at the predicted sites, and the SCXRD and DFT structures were in good agreement (Supplementary Table 2). For the sites with probability around 0.7-0.9, we would normally consider them as well, but the Cu 11 and Cu 12 clusters are much smaller and their structures are much simpler, quite resembling the structures in our training set. So the most probable three sites from our machine-learning model happen to be the most viable model that agrees with the SCXRD and is further confirmed by DFT.
In Cu 11 , three hydrides are disposed in the center of Cu 4 square with an approximate square pyramidal μ 5 coordination mode. Of note, the positions of the three μ 5 -H in Cu 11 relative to the centers of Cu 4 squares differ slightly: the middle one is right in the center of Cu 4 square and the other two are deviated from the centers of Cu 4 squares (close to the OAc − ). The Cu-H distances were found in the range from 1.61(6) to 2.02(7) Å. In Cu 12 , three hydrides are disposed in the center of Cu 6 octahedra with a μ 6 -H coordination mode. Similar to that in Cu 11 , the middle H was found to be right in the center of Cu 6 octahedron, while the other two show offsets closing to the OAc − . The Cu-μ 6 -H distance in Cu 12 ranges from 1.71(8) to 2.02(6) Å.
Interconversion. Interestingly, it was found that the interconversion between Cu 11 and Cu 12 could be triggered by solvents. The interconversion involves the adding a Cu + ion to Cu 11 or leaving of a Cu + ion from Cu 12 . Dissolving Cu 12 in DMSO led to the leaving of a Cu + ion to form Cu 11 , while the reaction of Cu 11 with CuOAc (1 equiv) in CH 3 OH converted it back to Cu 12 . The interconversion was not affected by O 2 for the same interconversion was observed both in the air and under nitrogen atmosphere. The flexible arrangement of the N donors of Tf-dpf makes such an interconversion possible, which allows keeping stable ligation to metal ions in adjusting to the structural changes between Cu 11 and Cu 12 .
To better understand the cluster-to-cluster transformation process, we monitored the cluster core transformation process (Cu 12 to Cu 11 ) by ESI-MS measurements (Fig. 5a). The MeOH solution of Cu 12 features one prominent peak attributed to [Cu 12 H 3 (Tfdpf) 6 (OAc) 2 ] + . The freshly prepared Cu 12 solution in DMSO showed peaks corresponding to [Cu 11 H 3 (Tf-dpf) 6 (OAc) 2 ] + along with a weak peak attributed to [Cu 11 H 3 (Tf-dpf) 5 (OAc) 2 ] + within 5 min. Then the peaks of Cu 11 keep increasing with time, and after 120 min the spectrum features only prominent peaks of Cu 11 while the peak of Cu 12 disappeared, indicating the complete conversion from Cu 12 to Cu 11 . We then monitored a solution of Cu 12 in DMSO-d 6 at room temperature by measuring its 19 F NMR spectra at different times. A slight upfield shift of ∼2.6 ppm of Cu 12 and Cu 11 was found in DMSO-d 6 compared with in CD 3 OD. As shown in Fig. 5b, the signal at −61.24 ppm of Cu 12 gradually disappeared, while that at −60.68 ppm of Cu 11 gradually grew with increasing time, indicating a transformation of Cu 12 to Cu 11 in DMSO at room temperature. Based on the integration ratios relative to the internal standard, the conversion of Cu 12 to Cu 11 is virtually quantitative.
Given the different numbers of Cu atoms in Cu 11 and Cu 12 , the transformation between the two clusters is not isomerization. As shown in (Supplementary Fig. 10), the OAc − only binds two out of the three terminal copper atoms of the Cu 12 core, and the other one copper atom could be regarded as an unsaturated site. Thus, it is hypothesized that the transformation of Cu 12 to Cu 11 is attributed to the binding ability of DMSO, which anchors on the unsaturated copper atom and removes it from the cluster. As a result, the binding mode of Tf-dpf in Cu 12 is distorted motif A, which leads to the twisting of two Cu 3 units and then the framework rearrangement to form Cu 11 (Fig. 6). Moreover, the conversion of Cu 11 to Cu 12 through adding CuOAc in CH 3 OH proves that Cu 11 is likely to combine free Cu ions to generate Cu 12 (Supplementary Fig. 11).
Hydrogenation catalysis. Synthesis of anilines or amines from the corresponding nitro compounds is an important process in both of the laboratory and the chemical industry due to their versatility in several biologically active natural products, pharmaceuticals, and dyes 41 . Transition metal-catalyzed hydrogenation is an important route for the transformation of nitro groups to amine groups 42,43 . Thus, the reduction of 4-NP to 4-AP by NaBH 4 was chosen as a model reaction to investigate the catalytic performance of Cu 11 and Cu 12 . Considering that Cu 11 and Cu 12 are insoluble in water, this catalytic reaction belongs to heterogeneous catalysis. The reduction process monitored by measuring the intensity change of 400 nm peak (4-NP) in UV/vis absorption spectroscopy. As the catalytic reaction proceeded in the presence of Cu 11 , the intensity of 400 nm peak decreased rapidly and disappeared within 10 min (Fig. 7a), indicating the complete conversion of 4-NP to 4-AP (λ max = 295 nm in water). In comparison, only 5% 4-NP could be reduced to 4-AP with equivalent Cu 12 catalyst even when the time was extended to 30 min, and the completion of reduction of 4-NP to 4-AP needed 10 h ( Supplementary  Fig. 12). It is quite interesting that two copper hydride clusters with similar structures show distinctly different activity in the hydrogenation reaction (Fig. 7b), which prompts us to pay efforts in mechanism study in terms of the role of hydrides.
Three major steps were generally thought to be involved in transition metal-catalyzed reduction of 4-NP to 4-AP 41,44 , and the formation of [M]-H species as well as the B-H bond cleavage was considered to be the rate-determining step. Therefore, we carried out an experiment using Cu 11 and Cu 12 as the catalysts for the reduction of 4-NP to 4-AP with NaBD 4 in place of NaBH 4 . In the cases of Cu 12 , no peak belongs to deuterated cluster was found in the ESI-MS spectrum after catalysis with NaBD 4 (Fig. 7c), which indicates that the encapsulated μ 6 -H of Cu 12 were shielded from interaction with substrates. Therefore, Cu 12 showed very low catalytic activity. On the contrary, the ESI-MS of Cu 11 after catalysis with NaBD 4 showed new peaks at 2762.6 and 2844.6 in addition to the expected peak of 2484.6 ([Cu 11 H 3 (Tf-dpf) 5 (OAc) 2 ] + ) (Fig. 7d). These two new peaks could be attributed to [Cu 11 HD 2 (Tfdpf) 6 (OAc)] + and [Cu 11 HD 2 (Tf-dpf) 6 (OAc) 2 + Na] + , respectively, which indicates that hydrides in Cu 11 were replaced by D atoms from NaBD 4 , i.e., the μ 5 -H species of Cu 11 were involved in the catalytic cycle. These facts reveal that the high catalytic activity of Cu 11 is related to the formation of μ 5 -H species on the cluster. Moreover, it is noted that Cu 11 is relatively robust and can be re-used after centrifugation. Even after seven cycles, Cu 11 retains its high activity (Supplementary Table 3). Previously reported copper hydride clusters including Stryker's reagent are usually moisture-and air-sensitive. Other copper hydride clusters such as [Cu 3 H(dppm) 3 (OAc) 2 ] 45,46 and [Cu 8 H 6 (dppy) 6 ](OTf) 2 7 are stable in solution for less than 3 days. Cu 11 and Cu 12 are stable in CH 2 Cl 2 for at least 2 weeks, their good stability makes them promising copper hydride catalysts for various applications.
Overall, Cu 11 and Cu 12 present a pair of valuable copper hydride clusters for correlating the structures and properties. They have identical amidinate ligands, similar metal atom arrangement, but different hydride location and distinct catalytic performance, which demonstrates the importance of the location of hydrides for efficient hydrogenation catalysis 45 . This information will be instructive in the design, synthesis and selection high performance hydrogenation catalysts.

Discussion
In summary, we have synthesized two stable copper hydride clusters Cu 11 and Cu 12 with the flexible amidinate ligand Tf-dpf. Because the multidentate amine ligand Tf-dpf has a negative charge and four binding N donors, it could provide strong binding to metal centers. Such a strong protection of ligand shell favors the high stability of copper hydride clusters. The compositions of these two title clusters have only one copper atom difference, but their Synthesis of Cu 11 (Tf-dpf) 6 (OAc) 2 H 3 (Cu 11 ). In total, 3 ml CH 2 Cl 2 /DMSO (v:v = 5:1) mixture of Cu(OAc) (24 mg, 0.2 mmol), HTf-dpf (0.1 mmol, 33.4 mg), and excess Et 3 N (20 ul) was stirred for 5 min first, then H 2 SiPh 2 (0.1 mmol, 18 ul) was added. The solution color changed from green to yellow in 10 min. The mixture was stirred for 3 h and evaporated to remove the CH 2 Cl 2 solvent. The crude product was washed by 5 ml CH 2 Cl 2 /n-hexane (v:v = 1:4) for three times, then dissolved in 4 ml CH 2 Cl 2 . The resulted solution was centrifuged for 2 min at 9000 r/min, and the orange supernatant was collected and subjected to diffusion with n-hexane to afford light orange crystals after 2 days in 24 Catalytic reduction of 4-nitrophenol. The water solution of 4-NP (1 ml, 20 mM), Cu 11 or Cu 12 (1 mg) was mixed, and the mixture was stirred for 10 min at room temperature. Time-resolved UV-vis spectra were taken immediately after the addition of NaBH 4 solid (50 mg, 1.3 mmol). The progress of the reaction was tracked by monitoring the change in intensity of 4-NP peak at 400 nm as a function of time. After reaction of Cu 11 , the reaction solution was centrifuged, and the catalysts was washed with 3 ml H 2 O for three times. Then the catalysts solid was dried under reduced pressure and re-used as fresh. Neural network prediction of hydride sites. We employed the recently developed deep-learning model to predict hydride sites in our clusters. The model was based on CNN and trained on Cu-H clusters with hydride sites determined by neutron diffraction. This model takes as input the heavy-atom coordinates of a cluster from the single-crystal X-ray diffraction and then outputs the occupancy for each possible hydride site in the cluster. The training data are based on 23 different copper hydride clusters from the Cambridge Structural Database whose hydride locations have been determined by neutron diffraction. The 23 structures were further chunked into 674 boxes of possible hydride sites that were used for training of CNN. The details of the CNN and its architecture can be found in the previous work 39, 40 and their Supporting Information. The trained CNN can classify a possible site for hydride in a given cluster with accuracy higher than 94%. In the present work, the X-ray structures of the Cu 11 and Cu 12 clusters (namely, coordinates of Cu, C, N, F, and O in the cluster) were used as input into the machinelearning model which then predicted hydride occupancies and ranked the hydride sites. Since there are only three hydrides in the Cu 11 and Cu 12 clusters, one can simply pick the top-ranked sites and examine the top three by inspection, followed by DFT geometry optimization for confirmation using the VASP code.
Physical measurements. UV-Vis absorption spectra was recorded on cary5000. Mass spectra were recorded on a high-resolution Fourier transform ICR spectrometer with an electrospray ionization source in positive mode. Nuclear magnetic resonance data were recorded on a Bruker Avance II spectrometer (500 MHz).
X-ray crystallography. Intensity data of compounds Cu 11 and Cu 12 were collected on an Agilent SuperNova Dual system (Cu Kα) at 173 K. Absorption corrections were applied by using the program CrysAlis (multi-scan). The structures of Cu 11 and Cu 12 were solved by direct methods. Non-hydrogen atoms except solvent molecules and counteranions were refined anisotropically by least-squares on F 2 using the SHELXTL program. For Cu 12 , the -CF 3 groups (F7-F9, F22-F24) were disordered over two sites with an occupancy factor of 0.5/0.5. SQUEEZE routine in PLATON was employed in the structural refinements due to large solvent voids. In addition, isor and rigu constraints have been applied due to geometric requirements of the ligands.
Computational methods. DFT calculations were performed with the quantum chemistry program Turbomole V7.1 47 . The Def2-SV(P) basis sets 48 were used for C, N, O, H, F. The Def2-TZVP basis sets 49 were used for Cu. Geometry optimization was done with the functional of Perdew, Burke and Ernzerhof 50 .

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request. The X-ray crystallographic coordinates for structures reported in this article (see Supplementary Table 1