Mapping Solvation Environments in Porous MetalâOrganic

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Mapping Solvation Environments in Porous Metal Organic Frameworks with Infrared Chemical Imaging Ayanjeet Ghosh, Prabuddha Mukherjee, Sanghamitra Deb, and Rohit Bhargava J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.7b02104 • Publication Date (Web): 12 Oct 2017 Downloaded from http://pubs.acs.org on October 13, 2017

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The Journal of Physical Chemistry Letters

Mapping Solvation Environments in Porous Metal Organic Frameworks with Infrared Chemical Imaging Ayanjeet Ghosh[+]a, Prabuddha Mukherjee[+]a, Sanghamitra Deba, Rohit Bhargava*,a,b a

Beckman Institute of Advanced Science and Technology, University of Illinois at UrbanaChampaign, Urbana, IL 61801, USA b

Departments of Bioengineering, Chemical and Biomolecular Engineering, Electrical and Computer Engineering, Mechanical Science and Engineering and Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

[+]

Authors contributed equally to the work *Corresponding author email: [email protected] Abstract We report here the first mesoscale characterization of solvent environments in the Metal Organic Framework Cu3(BTC)2 using infrared imaging. Two characteristic populations of the MOF structures corresponding to the carboxylate binding to the Cu (II) (metal) ions were observed, which reflect a regular solvated MOF structure with axial solvents in the binuclear copper paddlewheel and an unsolvated defect mode that lacks axial solvent coordination. Infrared imaging also shows strong correlation between solvent localization and the spatial distribution of the solvated population within the MOF. This is a vital result as any remnant solvent molecules adsorbed inside MOFs can render them less effective. We propose fast IR imaging as a potential characterizing technique that can measure adsorbate and defect distributions in MOFs.

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Introduction Metal–organic frameworks (MOFs) are a relatively new class of hybrid materials with extensive potential as absorption devices, chemical and biological sensors.1-5 MOFs are polymers formed by coordination between metal ions with polydentate organic linkers, producing extended threedimensional structures, with a porous network1. Sorption properties of MOFs have been extensively investigated, and applications for gas/vapor separation, catalysis, and drug storage and delivery have been proposed3-5. The extent and the availability of the porous network of the MOFs is responsible for its efficacy in devices or sensors. Anomalies within the device that restricts the size and availability of pores would render it inefficient. These may arise due to a) the formation of defects where the linkage between the ‘metal’ and the ‘organic framework’ is disrupted6, leading to faulty propagation of the crystal structure over the base template, or b) coordination of the adsorbed solvent/impurities to the metal centers. The latter case is of particular significance: an essential post synthetic modification of MOFs is activation, wherein the MOF material is heated under vacuum to expel any adsorbed solvent7. A MOF device containing residual solvent will show reduced adsorption, for example, which will limit its application. A MOF device affected with faults may go undetected unless properly characterized. Typical methods for assessing activation efficiency includes techniques such as nitrogen adsorption isotherms and thermogravimetric analysis8. Specific surface areas obtained by nitrogen adsorption isotherms of HKUST-1 show wide variability between different synthetic and activation procedures, which shows that that not all activation approaches are equivalent. Characterization techniques like Electron microscopy, Atomic Force Microscopy (AFM) and XRay diffraction studies can measure the morphology and crystalline defects in MOFs but are generally insensitive to adsorbates or their chemical identity in MOF pores. These approaches may be destructive, and hence can be limited in their applicability towards MOF devices. Most importantly, structural understanding does not offer insight into the chemistry of MOFs which is at the heart of their utility. MOFs are naturally heterogeneous materials, having a distribution of pore and crystal sizes along with crystalline defects in the structure9. These factors lead to complex adsorption behavior of MOFs, which may not be homogeneous over the entire active area of a MOF device. Unfortunately, most studies have treated MOFs as ideal crystalline materials, which partly stems from the lack of appropriate characterization techniques: traditional infrared spectroscopic approaches like Fourier Transform infrared (FTIR) spectroscopy average over spatial domains as large as a few millimeters; thus, heterogeneity on the mesoscale is masked and spectral evidence is interpreted as homogeneously distributed within the measured sample. To better predict and understand the adsorption properties of MOF films and devices, we first need to evaluate spatial heterogeneities of the adsorbate at the pertinent length scale and its correlate to morphology and properties. Adsorption isotherms, typically used to evaluate the surface area of MOFs cannot correlate the availability of pore sites to MOF structure and morphology. Currently there exists no standard technique that can address these issues and unequivocally ascertain spatial homogeneity, or lack thereof, in MOFs in regard to their sorption properties. Hence there is an unmet need to examine MOF structure and sorption chemistry to complement the structural and morphological information of traditional characterization techniques. Here we seek to understand the spatial variation in MOF adsorption that can affect function by using emerging infrared (IR) spectroscopic imaging techniques. IR spectroscopy has provided average composition within MOFs10-14 since commonly used organic linkers like carboxylates and adsorbates like water both have well-defined strong vibrational modes15, with spatial


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information provided by electron microscopy. In imaging, the sensitivity of vibrational IR frequencies to local molecular composition and electrostatic environments is combined with microscopy to map chemical heterogeneities on a micron scale. IR microscopy thus offers one alternative to existing techniques, resolving several microns and covering centimeters. The primary advantage of using IR imaging is that it offers a label free, non-destructive insight into the MOF chemistry, and can therefore be the perfect complement to the higher resolution, morphological techniques like electron microscopy on one hand and a significant extension of IR spectroscopy to sample heterogeneity on the other. However, spectroscopic imaging is a slow process due to the need for both large spatial and extensive spectral coverage and not suitable for rapid or detailed characterization. To overcome these challenges, we use discrete Frequency IR imaging (DFIR) using emerging tunable quantum cascade lasers (QCL) that can be tuned to be sensitive to MOF structure and the adsorbate molecules. Specifically, we analyze HKUST-1 that is a framework formed from [Cu3-(BTC)2] (BTC = 1,3,5benzenetricarboxylic acid) units and is one of the most extensively studied MOFs11-12, 14, 16-17. The structure of HKUST-1 is a binuclear paddle-wheel where the carboxylate moieties form bridging coordination linkages with the copper centers16 (Figure 1A). At wavelengths relevant to the carboxylate vibrational modes, we demonstrate the spatial variation of MOF spectra and correlate them to structural states differing in solvent coordination. The absorbance at specific vibrational modes provides for quantitative concentration measurements of specific chemical moieties, whereas solvent coordination states manifest themselves in the vibrational spectrum as frequency shifts. Since the solvent molecules trapped inside the pores alter the bridging between copper and carboxylate, these shifts and absorbance provide a complete molecular description at every pixel. The spatial distribution of concentration and coordination is enabled by imaging; together, the spatial-spectral analysis leads to a rapid and robust characterization of MOF adsorption efficiencies.



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Figure 1. (A) Crystalline structure of the unit cell of HKUST-1 MOF and, (B) its SEM image. (C) An average infrared spectra of the MOF. The inset shows the spectral variation of the Carboxylate stretching modes from different regions of the deposited MOF after performing imaging, demonstrating a potential diversity -1 whose extent cannot be estimated. (D) Image of MOF absorbance at 1378cm , directly visualizing spatial variation within the MOF. The ration of high-low absorbance areas provides a quantitative and rapid estimate of the functional efficiency of the MOF.

Synthesized MOF particles were characterized using Scanning Electron microscopy (SEM) and IR spectroscopy. Figure 1B shows a representative SEM image of the synthesized HKUST-1 crystals. The crystalline particles, clearly visible in the SEM image are characteristic of HKUST-1 frameworks.18-19 The average IR spectrum of HKUST-1 in the fingerprint region (800-1800 cm-1, where carboxylate and ring vibrations of the ligand absorb) is shown in Figure 1C, where the carboxylate stretching peaks coordinated with the metal are evident between 1350-1500 cm-1 and the ring vibrations ring bending vibrations of the phenyl moiety appear at 1650 cm-1.15 These three bands are characteristic of HKUST-1;11-12, 18 however, the average chemical information in the spectrum cannot be directly correlated to the spatial information from the SEM images, which underscores the need for “chemical” imaging in MOFs. QCL-based IR microspectrometers20-22 can acquire images at specific frequencies with speeds ~1mm2/minute/wavelength with high signal to noise ratios, providing rapid and accurate imaging. For HKUST-1, an absorbance image at 1378 cm-1 (Figure 1D) represents a means for quantitative evaluation of concentrations and its variation in a sample. The absorption band at ~1378 cm-1 shows varying degrees of asymmetry at different spatial locations as shown in Figure 1C inset. The ~1450 cm-1 band however, does not show any clear asymmetry as the 1378 cm-1 band (see Supporting Information). The spectra indicate the presence of two underlying sub-states, which is not obvious from the spectrum in Figure 1C, which is the average across many spatial locations. To understand this chemical heterogeneity, a spectral fitting can be undertaken as performed in conventional spectral analysis. The 1378 cm-1 band fits well to two overlapping Voigt profiles, confirming the existence of two underlying populations at ~1374 cm-1 and 1382 cm-1 (for details of the fitting procedure, see Supporting Information).


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Visualization of the two populations is possible by inspecting images at individual frequencies as show in Figures 2A and B for the absorbance of 1374 cm-1 and 1382 cm-1 respectively. The populations corresponding to these sub-states are heterogeneously distributed: the 1374 cm-1 population is prevalent in specific regions, whereas the 1382 cm-1 state is evenly distributed. The corresponding absorption images for another sample preparation are shown in Figures 2E and 2F respectively. While not as pronounced as the first sample, the spatial distributions of the two frequencies are present. Interestingly, we note that asymmetric lineshapes for the carboxylate symmetric stretch has been observed and reported23, but has not been investigated

-1

-1

Figure 2. The distribution of the carboxylate modes at 1374 and 1382 cm and methanol at 1012 cm for two different regions are shown in the following figure. A-H show the distribution of the carboxylate modes -1 at 1374 and 1382 cm for the two regions pre- and post- activation. I-L show the distribution of methanol in region 1 and 2 before and after activation.

in detail. Indeed, due to the lack of knowledge of the spatial variance of spectra, it is not possible to unequivocally attribute them to structural variations or localized features in samples that are heterogeneous. To verify that the absorption images at the frequencies mentioned above truly reflect the relative populations of these sub-states, a blind signal deconvolution algorithm, Non-negative Matrix Factorization (NMF), was used to identify the underlying spectral components. NMF is a matrix decomposition approach24-25, which decomposes a non-negative matrix into two low-rank non-negative matrices (for details, see Supporting Information). NMF decomposition of the hyperspectral image from 1330 cm-1 and 1400 cm-1 into 2 factors reveals component spectra that peak at 1374 cm-1 and 1382 cm-1. The spatial distributions of these factors correlate strongly with the absorption images, indicating that the latter do indeed capture thee spatial variance of these underlying states.

The observation of a doublet due to two distinct underlying states for the symmetric stretch inspires the search for its origin. One explanation can be the presence of unreacted BTC ions.



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However, it is well known that coordination to metal ions generates frequency shifts that are significantly larger15, and also affects ωsym - ωasym, the frequency splitting between the symmetric and asymmetric carboxylate stretches. Since we do not observe a second peak for the asymmetric stretch band at ~1450 cm-1, this possibility can be ruled out. This evidence also rules out the possibility of coordination defects (e.g. bidentate/unidentate in lieu of bridging), as those conformations are also expected to have larger frequency shifts and splittings15. A second explanation is to consider the local solvation environment of a vibrational probe and its effect on the characteristic vibrational frequency26-28 arising from electrostatic effects, in this case ranging from no solvent to fully solvated regions. In mesoporous materials like MOFs, this is particularly relevant as the solvent can be trapped in the porous network. For sub populations at lower frequencies by ~8-10 cm-1 a typical attribution is to hydrogen bonding and/or solvation26. Here, the carboxylate stretch can exhibit a frequency shift due to the solvent (in this case methanol), leading to the two states observed in our experiments. Absorption images corresponding to C-O stretching frequency of methanol at ~1012 cm-1 are shown in Figures 2I and 2K.18 If indeed the sub-states were due to different solvation environments, we can expect a correlation between one of the component images and the methanol absorption image. While the methanol absorption image correlates better with the image at 1382 cm-1, neither component matches perfectly with the methanol distribution. This observation, however, does not rule out the possibility of different solvation states, as the solvent adsorbed in MOF cavities can be in various configurations that may or may not interact with the Cu-BTC network. Thus, presence of methanol does guarantee coordination to the metal centers, and hence a significant frequency shift. To verify the preceding hypothesis, the samples were therefore subjected to an additional activation step7, which expels part of the adsorbed solvent. Infrared images after activation at 1374 cm-1, 1382 cm-1 and 1012 cm-1 are shown in Figures 2C, 2D and 2J (2G, 2H and 2L for region 2) respectively. It can be clearly seen that upon activation, absorbance at 1012 cm-1 is significantly reduced, indicating effective expulsion of methanol. Furthermore, absorbance at 1382 cm-1 is markedly lower whereas the image at 1374 cm-1 remains practically unchanged. This observation is consistent with the hypothesis that the two states observed here are due to different solvent environments, and suggests that IR absorption images can be utilized to interpret solvent distributions inside the MOF network. This is similar to observations of vibrational frequency shifts in proteins due to change in local solvation26, 28-30.


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Figure 3. (A) With and (B) without solvent coordination representations of the MOF crystal structures. (C) -1 and (D) show the absorbance difference between the carboxylate modes at 1374 and 1382 cm for an area of ~ 5 mm x 3 mm after 1 hour and overnight activation respectively.

The loss in 1382 cm-1 absorbance is a somewhat unexpected result as we expect the redshifted component, representative of the direct solvent interactions, to be centered at 1374 cm-1. For a well-formed HKUST-1 network, the carboxylate moieties are ligated to the copper centers forming a paddlewheel structure, and the number of free carboxylates is lower, leading to very few hydrogen bonding opportunities between carboxylate and methanol, which would generate the above-mentioned redshift. On the other hand, methanol can coordinate to the empty axial sites of the binuclear copper paddlewheel, as shown in Figure 3A. It can be expected that when methanol coordinates to the copper centers, the electron density donated to the positively charged copper atoms is shared between methanol and BTC, resulting in a higher COO- bond order, and therefore higher frequency. On the other hand, in the absence of methanol coordination (Figure 3B), the electron density donated to the copper atoms is entirely from the BTC, resulting in a longer carboxylate bond, and lower vibrational frequency. The IR spectrum of Cu(benzoate).3H2O shows spectral signatures of both coordinated and free benzoate ions, for example31. Our observations represent a scenario where the two kinds of carboxylates are both coordinated to copper, but differ in the bond strengths resulting from solvent coordination.



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It has also been reported that upon activation, the binuclear paddlewheel motif undergoes contraction, leading to shorter metal-carboxylate bonds32. This can also contribute to the observed frequencies. Nevertheless, both possibilities point to a model where the two populations observed are indeed different solvation states, and their relative distributions report on the adsorbed solvent locations in the MOF network. We propose that the approach and resulting insight here can be extended towards characterizing adsorbates in MOF devices by simply acquiring the necessary IR images. This approach is particularly useful, as it does not require the adsorbates to be imaged directly and thus strong infrared absorption cross-sections of adsorbates is not necessary for them to be characterized. To demonstrate the applicability of this approach, we have imaged a ~5x3 mm2 area under two conditions: activated under nitrogen for 1 hour and overnight respectively. The image acquisition requires ~1.45 mins per frequency. For both cases, the methanol absorption is too weak to discern from scattered light/noise. But focusing on the MOF carboxylate band, which represents the spectral region with the highest signal to noise, the relative loss of adsorbed methanol between the two conditions can be easily and rapidly mapped out, as shown in the difference images (A1374-A1382) in Figures 3C-D. Taken together, our results provide facile mapping of spatial distribution of local solvation environments that differ with respect to axial coordination of solvent. To the best of our knowledge, this is the very first report of identifying mesoscale solvation environments in MOFs using IR microscopy and its ability for rapid, routine insight into the chemistry of MOFs. While one instance IR imaging of MOFs utilizes AFM coupled infrared photothermal imaging33, large area scanning is not possible and the insight is limited to examining interactions at small length scales. In contrast, IR imaging allows rapid evaluation of solvent distributions in MOF networks; even for adsorbates/solvents that are weak absorbers or infrared inactive, imaging at a few specific discrete frequencies of the organic ligand can furnish the spatial distribution of adsorbates. While here we have demonstrated the applicability of IR imaging towards mapping residual solvents in MOFs, the same approach can be extended to explore adsorbate loading as well. Furthermore, in addition to limited efficiencies of MOF devices due residual solvents, presence of structural defects can further hinder their adsorption properties. Defects in MOFs primarily arise from irregular coordination between the metal centers and the ligands, leading to unsaturated metal centers and/or uncoordinated ligands. IR spectroscopy is sensitive to the binding state of the ligands, and imaging can thus be used to characterize these defects if needed. The work provides a convenient route to understand the applications of imaging towards assessing the chemical composition and physical state of MOFs. The ability to rapidly map out adsorbates in a MOF network can pave way to real-time kinetics measurements that have significant potential towards forwarding our knowledge on the viability of MOFs for applications like heterogeneous catalysis.

Materials and Methods Synthesis of HKUST-1 MOF: The HKUST-1 crystals were synthesized following reported protocols18. All chemicals were purchased from Sigma Aldrich and used without further purification. A precursor solution (1.22g copper nitrate (99.99%) and 0.58g 1,3,5benzenetricarboxylic acid (95%) was dissolved in 5g DMSO (99%)). 200 µ L of the precursor solution was added to methanol (10 mL, 99.9%) with stirring over 1 min that continued for 10 additional minutes. The precipitate was isolated by centrifugation, washed with methanol and deposited on low-E slides for infrared imaging and onto SEM holders for electron microscopy.


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Samples were dried under nitrogen before experiments. For activation, samples were heated in a nitrogen purged oven at 70°C overnight. Infrared Spectroscopy and imaging: A QCL-based single point detector system, a prototype developed by Agilent equipped with a 0.72 NA refractive objective and a room temperature bolometer detector, was used for the IR measurements. The wavelength range for all the spectral scans were set to 800-1900 cm-1 (with 128 co-additions), while for the images were set to 1330 to 1500 cm-1 for the carboxylate bands and 970-1060 cm-1 for the methanol absorption band. The spectral resolution for both the images and the spectra were 2cm-1. All the fitting procedures, image registration and analysis were carried out with the Matlab R2016 software. Scanning Electron Microscopy: All the SEM images were acquired with a Philips XL30 ESEM-FEG environmental scanning electron microscope at the Imaging Technology Group at the Beckman Institute for Advance Science and Technologies. The samples were coated with an Au-Pd coating and were imaged at a potential of 5KV.

Acknowledgment This work was supported by the Agilent Thought Leader Award to R.B. by Agilent Technologies Inc. Supporting Information Available: Spectral fitting parameters, details of the Non-negative Matrix Factorization analysis and spectra of both the symmetric and asymmetric ligand stretching modes.

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Mapping Solvation Environments in Porous MetalâOrganic

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Mapping Solvation Environments in Porous MetalâOrganic