Diffusion-programmed catalysis in nanoporous material

Diffusion-programmed catalysis in nanoporous material | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diffusion-programmed catalysis in nanoporous material Ritesh Haldar, Suvendu Panda, Tanmoy Maity, Susmita Sarkar, Arun Manna, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5076569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract In the realm of heterogeneous catalysis, the diffusion of reactants into catalytically active sites stands as a pivotal determinant influencing both turnover frequency and geometric selectivity in product formation. While accelerated diffusion of reactants can elevate reaction rates, it often entails a compromise in geometric selectivity. Porous catalysts, including metal-organic and covalent organic frameworks, confront formidable obstacles in regulating reactant diffusion rates. Consequently, the chemical functionality of the catalysts typically governs turnover frequency and selectivity. This study presents an approach harnessing diffusion length to achieve improved selectivity and manipulation of reactant-active site residence time at active sites to augment reaction kinetics. Through the deployment of a thin film composed of a porous metal-organic framework catalyst, we illustrate how programming reactant diffusion within a cross-flow microfluidic catalytic reactor can concurrently amplify turnover frequency (exceeding 1000-fold) and enhance geometric selectivity (~ 2-fold) relative to conventional nano/microcrystals of catalyst in one-pot reactor. This diffusion-programed strategy represents a robust solution to surmount the constraints imposed by bulk nano/microcrystals of catalysts, marking advancement in the design of porous catalyst-driven organic reactions. Physical sciences/Materials science/Materials for energy and catalysis/Metal–organic frameworks Physical sciences/Materials science/Materials for energy and catalysis/Porous materials Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Porous heterogeneous catalysts, like zeolites, metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) are widely researched for organic conversions. 1-3 The growing interest particularly in MOFs, constructed by linking metal ion/cluster with functionalized organic ligand, stems from their well-organized confined active sites, which are sterically and electronically tunable. 4-6 The extensive chemical diversity and predictable structure-property relationships of MOF-based catalysts have significantly expanded their applications, including in the areas such as enantiomer/size-selective catalysis, electrocatalysis, and photocatalysis. 7-15 Nonetheless, a persistent challenge for porous catalysts is the limitation imposed by mass transfer or diffusion. 16,17 Often all the catalytic sites are not accessible due to substantial diffusion barrier (surface barrier and large diffusion length), leading to lowering of conversion efficiency. 16 Further, poorly regulated diffusion leads to smaller geometric selectivity. Therefore the overall efficiency of porous catalysts remains underestimated. Although the diffusion mechanism 18-20 is well-explored in theory and simulation for porous materials, access to the highest efficiency, selective catalysis in practice remains a complex challenge. In typical catalytic reaction, whether batch or fixed-bed types, a range of MOF particle sizes is utilized. This size heterogeneity can significantly reduce both the turnover frequency (TOF) and geometric selectivity (which is the hallmark feature of MOF catalysis 21-23 ). The underlying reasons are as follows: in heterogeneous catalysts, active sites are located both on the surface and within the bulk. When the catalyst has a very high surface-to-volume ratio (particle size 10 μm), the very high reactant diffusion barrier limits the reaction at the surface active sites. Within the intermediate particle size range, the diffusion rate controls the reaction outcome, influencing both TOF and selectivity. This relationship is depicted in Figure 1a-b, demonstrating that for a specific catalytic reaction, there is a critical particle size or diffusion length ( L c ) at which geometric selectivity is maximum (i.e. selectivity is controlled by diffusivity only). However, achieving this critical point in a conventional catalytic reaction appears unattainable. It is also noteworthy that maximizing geometric selectivity inherently leads to reduction in TOF; a trade-off intrinsic to any porous material-based catalytic reaction. There are elegant chemical pathways (defect density control, 24-27 postsynthetic linker modification, exchange 28-30 and multivariate assembly 31,32 ) that can uplift TOF or geometric selectivity in MOF catalysis. For other types of porous catalysts (e.g. zeolites), the chemical tunability is not always straightforward. Keeping the chemical compositions constant in a MOF catalyst, we have explored the possibility of efficiency (TOF and selectivity) tuning by controlling the diffusion length only. We realized that the primary factors are diffusion length ( L D ) and reactant-active site residence time (collision frequency). The former can improve geometric selectivity, while the latter can boost TOF. To precisely manipulate L D , we have utilized a monolithic thin film of MOF catalyst with programmable thickness, and mounted in a microfluidic cell (cross-flow) to enhance residence time. This reaction setup is illustrated in Figure 1c (i). By precisely controlling the L D and residence time, for a condensation reaction we have achieved a >1000 fold increase in TOF and 2-fold enhanced geometric selectivity compared to a chemically equivalent batch reaction using submicron-size particles of catalyst. A straightforward control over L D and residence time allows programming the diffusion of the reactants, which controls the outcome of the reaction, as depicted in Figure 1c (ii). In the following discussion we have illustrated the working principle and a general strategy to perform the diffusion-programmed catalysis, enabling access to the highest limits of TOF and geometric selectivity for any porous catalyst. Results and discussion MOF catalyzed condensation reaction The pore window size, cavity size and chemical environment of MOFs can be engineered precisely and predictively. 33 – 36 By leveraging this chemical and structural tunability, catalytic sites within the pores can be tailored to accommodate specific reactants. This exceptional selectivity in geometry can be regulated by varying the L D of the reactants. Literature suggests that reducing L D (i.e., using smaller particle sizes) enhances the turnover frequency (TOF). 37 – 39 However, this increase in TOF comes at the cost of reduced geometric selectivity. To evaluate this statement, we conducted a conventional one-pot reaction using submicron-scale catalyst particles. We investigated a Knoevenagel condensation reaction catalyzed by a Lewis base (-NH 2 ) housed within a robust MOF pore. 40 , 41 The MOF of interest, UiO-66-NH 2 , 42 composed of Zr 4+ and 2-aminobenzendicarboxylic acid (NH 2 -bdc), was synthesized, with particle sizes 43 averaging ~ 500 nm and ~ 160 nm employed for the catalytic reactions (see supporting information, Figure S1 -S4). The MOF's triangular pore window size is ~ 6 Å and cavity diameter is ~ 11 Å (Figure S1 ). Ethyl cyanoacetate ( Et -CA; 4.5 Å × 10.3 Å) and tert-butyl cyanoacetate ( t-But -CA; 5.8 Å × 10.3 Å) were chosen as the small and large nucleophiles, 40 respectively, reacting with benzaldehyde (6 Å × 4.3 Å) to form ethyl-2-cyano-3phenylacrylate ( Et -Acr) and ethyl-2-cyano-3phenylacrylate ( t-But -Acr), respectively (Fig. 2 a, S5-7). The nucleophiles' dimensions allow diffusion into the pores, albeit at varying rates due to sieving effects. Molecular dynamics (MD) simulations were employed to estimate diffusivity differences of two nucleophiles within UiO-66-NH 2 (see methods section below). Analysis of mean square displacement (MSD) profiles indicates that Et -CA diffuses approximately ten times faster than t-But -CA (Figure S8a). Close-proximity interactions between nucleophiles and MOF pores reveal that the active -CH 2 group (marked with * in Fig. 2 a) of nucleophiles predominantly interacts with the organic linker rather than the metal node (Figure S8b-c). This -CH 2 - NH 2 interaction suggests the potential for Lewis base catalyzed reactions. We anticipated the reaction rate to exceed the diffusion rate, leading to diffusion-controlled TOF. At 24 h, we achieved 42 (± 2) % and 24% conversions for the smaller and larger nucleophiles, respectively, using the ~ 160 nm catalyst particles (1 mol% in ethanol, 70°C; Table S1 , Figure S9-11). Under similar conditions, the ~ 500 nm catalyst particles exhibited approximately 50% lower yield but improved selectivity (~ 25%) for the smaller nucleophile. However, this improvement in selectivity is not deemed significant. Noteworthy, the one-pot reaction contains a large variety of particle sizes (Figure S2-3). Hence any significant change in the selectivity by varying the particle sizes is a challenge. Cross-flow microfluidic MOF catalysis At this point, to regulate TOF and selectivity, we have designed a new reaction methodology. To enhance the TOF, we have done following: i) a method to improve TOF is by increasing collision frequency (reactant-active site). To achieve this, we designed a microfluidic reactor where the catalyst is supported on a solid surface ( vide infra ) and the volume of reactant solution in contact with the catalyst is 80 (± 4) µL. The small volume ensures that only a limited amount of reactant interacts with the entire catalyst layer at any given time, thereby enhancing reactivity. ii) The microfluidic cell is connected to a pump that circulates a larger volume of reactant solution. Consequently, both reactants and the formed products circulate in a cross-flow direction along the catalyst bed (thin film). This reaction setup resembles one-pot catalysis reactions, except that the catalyst is placed within a microfluidic cell as a thin film. iii) Reactant flow can be controlled (0.1–15 mL/min) in a cross-flow direction, which helps preventing pore surface blockage. To enhance the selectivity, the catalyst is deposited as a monolithic thin film with controllable thickness (see experimental section, Figure S12). In the proposed cross-flow setup, concentration gradient is along the film thickness, and hence the thickness is L D . This allows straightforward tuning of the L D , unlike in the one-pot reaction ( vide supra ). The reaction setup is illustrated in Fig. 2 b. Noteworthy those conventional nano/microparticles of MOFs are not suitable for this catalysis reaction. Rather, recently developed MOF thin film growth methodologies, e.g. layer-by-layer epitaxy, 44 – 46 chemical vapor deposition, 47 solution atomic layer deposition, 48 vapor assisted conversion, 49 and electrochemical 50 , 51 deposition can be applied to make the catalyst layer. To execute the reaction scheme, we have synthesized UiO-66-NH 2 thin film (UiO TF1 ~120 nm thickness) on a Si/SiO 2 substrate at room temperature (298 ± 3 K), using a drop casting methodology 52 (Figure S13). This method is suitable for controlling the film thickness with high crystalline orientation (Figure S14). Using the catalyst thin film UiO TF1 we performed reactions using a similar reactant solution volume as for one-pot reactions, at 70°C using a flow rate of 5 mL/min (Fig. 2 b). We could realize maximum ~ 96% and 35% formation for the Et -Acr and t-But -Acr after 24 h (TOF ~ 2271 and 790 h − 1 respectively, Table S2, Figure S15-18). The TOF and selectivity are enhanced by ~ 1300 fold and 1.5-fold, compared submicron-sized particle based catalysis. These findings, depicted in Fig. 2 c, validate the simultaneous enhancement of TOF and selectivity through our proposed strategy. In absence of any chemical modification, these observations are unprecedented. 17 Subsequently, we elaborate on the underlying principles driving these radical improvements. Diffusion-programmed catalysis To confirm that the reaction is indeed controlled by pore diffusion and that selectivity enhancement is attributable the diffusion length ( L D ), we have conducted reactions varying film thickness. Increasing film thickness simultaneously increases L D and also amount of catalyst present. In the absence of diffusion control, the TOF should remain unchanged with increasing thickness. Conversely, a diffusion-regulated reaction should exhibit a quadratically decreasing TOF, assuming the nucleophiles obey Fickian diffusion (TOF \(\:\propto\:\) 1/ L D 2 ). We synthesized UiO TF2 and UiO TF3 with thickness ~ 300 (± 30) and 400 (± 50) nm, respectively (Figure S13). The film TOF vs film thickness profiles for both of the condensation products are shown in Fig. 3 a (Table S2, under similar reaction conditions). The data clearly show that with increasing thickness TOF decreases and selectivity increases in a nonlinear trend. This supports the notion that the conversion is controlled by intrapore diffusion rather than being surface confined. Furthermore, it is feasible to enhance selectivity by adjusting the film thickness, i.e. L D . Apart from selectivity, the sharp enhancement in TOF can be attributed to the microfluidic reaction set up. Direct evidence of this can be obtained by following experiment: A reaction of Et -CA and benzaldehyde was carried out in one-pot method using the thin film catalysts UiO TF2 . After 30 h the conversion was ~ 50% (Table S3, Figure S19). This markedly lower conversion efficiency compared to the microfluidic reaction, confirms that the effective residence volume for the reactant solution and entire catalyst indeed enhances the TOF. To verify influence of other factors, e.g. defect density in the powder and thin film, we compared the infra-red (IR) spectra and x-ray photoelectron spectra (XPS) of those (Figure S20-21). The IR spectra indicated that the –COO stretching frequencies (asymmetric and symmetric 1571and 1382 cm − 1 , respectively) are similar for thin film and powder catalysts. XPS confirmed that the Zr/N ratio and nature of defects related to dangling –COO are similar 53 , 54 for both the type of catalysts. We have also realized that monodispersed surface-anchored MOF particles can also enhance TOF. 10 We have confirmed that the UiO TF1 is a monolithic thin film, having no evident cracks or islands of crystals (Figure S13a). These above mentioned evidences support that the enhanced TOF is due to the integration of MOFs in a cross-flow microfluidic setup. After confirming the influence of diffusion and microfluidic reaction set up on the selectivity and TOF, respectively we have performed a flow-rate dependent catalysis reaction. Flow-rate and reactant-catalyst residence time is inversely proportional. In the absence of diffusion control, it is expected that product yield % will linearly decrease with increasing flow-rate. In case of diffusion regulated process, a quadratic decrease of conversion % is expected ( vide supra ). In Fig. 3 b, Et -Acr yield % vs flow-rate profile (4h reaction at 70°C) exhibited a nonlinear trend (yield % \(\:\propto\:\) (1/ F ) 0.2–0.32 ), confirming reactant diffusion limited reaction (Table S4, Figure S22-23). Moreover, t-But -Acr yield % also reduces nonlinearly (Table S4, Figure S24) with increasing flow rate, and we could observe highest selectivity of 11.5 at a flow-rate of 1 mL/min ( Et -Acr yield ~ 70%). Using similar conditions (solvent, temperature and concentration) in a one-pot reaction similar TOF and selectivity is not feasible. Noteworthy, the TOF and selectivity vs flow-rate profiles in Fig. 3 b resemble Fig. 1 b, except diffusion length is replaced by flow-rate. We have shown that by straightforward tuning of flow-rate it is feasible to achieve critical diffusion path ( L c ) which allows access to highest selectivity with higher TOF than the conventional methods. It is evident that for thickness and flow-rate based diffusion control experiments conventional Fickian diffusion is not followed, as the power law diverges from the quadratic norm. This anomalous behavior is indicative of additional factors, e.g. presence of competitive interaction with benzaldehyde and any specific chemical interaction with MOF. These factors inhibit the long-range random walk, leading to a modified TOF expressed as TOF \(\:\propto\:\) (1/ F ) 1/(2+α) , where α represents the anomalous factor. 55 – 57 The above discussed experiments conclude that the TOF and selectivity can be enhanced beyond the conventional limits using the proposed scheme. A comparison of the TOF and selectivity, for similar condition (at similar temperature, solvent and time) one-pot (using 500 nm particle) and cross-flow microfluidic (using UiO TF2 ) reaction, confirmed > 1000 and 2-fold enhancement, respectively (Table S4-5, Figure S25). In the next step, we have explored the impact of heterogeneous mixtures (i.e. mixture of Et -CA and t-But -CA) on the reactivity. In Fig. 4 we have illustrated the observed TOF and selectivity for one-pot (1 mol% catalyst) and cross-flow microfluidic (4.2×10 − 3 mol% catalyst, 0.1 mL/min) reactions (Table S6, Figure S26-S27). It is evident that competing diffusion of the reactants decreases the total conversion efficiency. However, the selectivity are found to be 5.4, higher than the one-pot reactions using large (4.3) submicron-sized catalyst particles. The improvement in the conversion efficiency is also evident; Et -Acr yield % is similar for one-pot (smaller particle) and cross-flow microfluidics methods, although catalyst mol% differ by > 1000-fold. This confirms that even in a heterogeneous mixture of competing reactant diffusion, cross-flow microfluidic reaction using MOF catalyst monolith is superior to the state-of-the-art catalysis reactions. Conclusion Metal-Organic Frameworks (MOFs) are esteemed as exceptional heterogeneous catalysts among the porous materials, owing to their crystallinity, extensive chemical versatility, high surface area, and confinement effects. However, the diffusion-limited TOF and selectivity present significant challenges that continue to impede their catalytic performance. To address these issues, we have engineered a cross-flow microfluidic reaction setup and catalyst monolith, which allow precise control over reactant diffusion, thereby modulating both TOF and selectivity. This diffusion programmability is exemplified in a Knoevenagel condensation reaction, demonstrating the feasibility of simultaneously enhancing TOF and product selectivity beyond the conventional limits imposed by one-pot or batch reactors, achieving improvements by orders of magnitude. The ability to control diffusion rate allows for continuous tuning of the conversion efficiency, with experiments revealing a sub-diffusive nature of the catalytic reaction. This marks the first proof-of-concept demonstration of diffusion-programmed catalysis in porous materials, representing a significant step forward in the field. Methods Characterization techniques: X-ray diffraction: The X-ray diffraction patterns of the powder and thin films were recorded using a Rigaku XDS 2000 diffractometer using nickel-filtered Cu K α radiation ( λ = 1.5418 Å). Data were collected from 5 to 20° at room temperature (voltage 40 kV, current 200 mA). XRD was recorded in 2 θ / θ (step size 0.01, scan rate 0.2°/s) geometry. Scanning electron microscopy (SEM): Morphology and cross-section of the thin films and powders were characterized using field emission scanning electron microscopy (FESEM), JEOL JSM-7200F instrument with a cold emission gun operating at 30 kV. Infrared (IR) spectroscopy: Infrared reflection absorption spectroscopy (IRRAS) of the thin films and attenuated total reflection (ATR) absorption spectroscopy of the powder were done using the Bruker Vertex 70v instrument, with a spectral resolution of 2 cm − 1 . IRRAS results were recorded in grazing incidence reflection mode at an angle of incidence 45 ° relative to the surface, under vacuum at room temperature. 1-octadecanethiol self-assembled monolayer (SAM) functionalized Au was used for background measurements. X-ray photoelectron spectroscopy (XPS): Elemental detection of the powder and thin film was performed using X-ray photoelectron spectrometer (PHI versaProbe III) within an ultrahigh vacuum (1×10 − 9 bar) environment. This instrument was equipped with an Al- Kα X-ray source and a monochromator. Nuclear magnetic resonance (NMR) spectroscopy: NMR spectra were recorded on a BrukerNanoBay 300 MHz NMR spectrometer. In our experiment, after the reaction, we used a syringe filter to separate the MOF powder catalyst. The entire collected solution was then concentrated and 0.4 mL of CDCl 3 was added to check the NMR data. For the cross-flow microfluidic catalysis, we collected the entire solution from the fluidic cell, concentrated and after adding 0.4 mL of CDCl 3 we checked the NMR data. Molecular dynamic simulation of reactant diffusion: Simulation Model: We considered a 1×1×1 UiO-66-NH 2 containing one molecule of Et -CA or t-But -CA individually. Partial charges for the MOF atoms were obtained from earlier report, 58 and other bond, angle, and dihedral parameters were modeled using OBGMX. The analyte molecules were modeled using Charmm force field generic parameters (CGenFF). 59 Simulations for each reactant molecule were conducted in the gas phase, within a rectangular box of dimensions 2.07 × 2.07 × 2.07 nm³. Simulation Method: Each simulation employed periodic boundary conditions (PBC) in all three dimensions. Long-range electrostatic interactions were managed using the particle mesh Ewald (PME) method 60 with cubic interpolation, with a 1.2 nm cutoff for short-range electrostatic interactions. The LINCS algorithm 61 was used to constrain bonds involving hydrogen atoms. The system was first energy minimized using the steepest descent algorithm, followed by stepwise equilibration over seven steps with gradual temperature increases from 50 K to 300 K, each step lasting 100 ps with a time step of 0.0005 ps. During equilibration, the average temperature was maintained using a V-rescale thermostat, 62 separately coupling the MOF and analyte molecule. The equilibrated system then underwent a 10 ns NVT production run at 300 K, also maintained by the V-rescale thermostat. All simulations were performed using GROMACS 63 version 2018.6 and repeated multiple times to ensure statistical reproducibility. Reactant diffusion was analyzed by calculating the mean square displacement (msd) using the 'gmx msd' tool, and the corresponding diffusion coefficient (D) was estimated. To understand the chemical interactions between reactant molecules and the MOF, pair correlation functions of active -CH 2 groups (see Fig. 2 a) were measured relative to specific functionalities in MOF (metal-oxo nodes, organic linkers, and -NH 2 group of the organic linkers). Declarations Acknowledgement: We acknowledge the intramural funding at TIFR Hyderabad from the Department of Atomic Energy (DAE), India, under Project Identification Number RTI 4007. References Pascanu V, González Miera G, Inge AK (2019) Martín-Matute, B. Metal–Organic Frameworks as Catalysts for Organic Synthesis: A Critical Perspective. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5076569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":356399660,"identity":"a458e728-2ebf-437f-8ce9-0bde725bf71a","order_by":0,"name":"Ritesh Haldar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3Qv0sDMRTA8Rce5JZcuz7pgf+CkuEqtPRfSTmwSzd3CQjPpeJq6Z/h4niS1T+hi8tNDidd7uAGU4tSsKmODvlCpuSTXwCx2H8M/RC2hB4YgBqoD7SbUGGCOyI9EQ+GTuyvBPYIKgPfJFh+239+bZ/WpzIpqs24GVK+srKuO8jyAMkcok5fqnNWlV7NDVG2LnG5ZFAX9jAhRDkQ7AST0ejJNZFBTC2oszJIkrZlN2GabXDoT/kkXXeUSEjZTZnmGuGL+N84RvTAk4LV25VYXG7J9EbcMYWJ/7B3f7HxfTJ7hGbkn0KFg6YbTULkUMJu9/r7+lgsFov96APoy00J4iMAFgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9697-9169","institution":"TIFR Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Ritesh","middleName":"","lastName":"Haldar","suffix":""},{"id":356399661,"identity":"c82044b1-a396-481c-bb1a-c71c2d63eb07","order_by":1,"name":"Suvendu Panda","email":"","orcid":"","institution":"TIFR Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Suvendu","middleName":"","lastName":"Panda","suffix":""},{"id":356399662,"identity":"d7d4d19e-c7b4-4488-bca4-7d05a5ccb874","order_by":2,"name":"Tanmoy Maity","email":"","orcid":"https://orcid.org/0000-0003-1901-1060","institution":"TIFR Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Tanmoy","middleName":"","lastName":"Maity","suffix":""},{"id":356399663,"identity":"1d2bf158-1373-45c3-9de0-01ded0a77004","order_by":3,"name":"Susmita Sarkar","email":"","orcid":"","institution":"Tata Institute of Fundamental Research Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Susmita","middleName":"","lastName":"Sarkar","suffix":""},{"id":356399664,"identity":"0b263580-cfb5-4d41-9162-04f20d291584","order_by":4,"name":"Arun Manna","email":"","orcid":"","institution":"TIFR Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Manna","suffix":""},{"id":356399665,"identity":"780925e9-3ee6-4f4d-8bd8-b6115694eefd","order_by":5,"name":"Jagannath Mondal","email":"","orcid":"","institution":"Tata Institute of Fundamental Research Hyderabad","correspondingAuthor":false,"prefix":"","firstName":"Jagannath","middleName":"","lastName":"Mondal","suffix":""}],"badges":[],"createdAt":"2024-09-12 09:35:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5076569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5076569/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-56575-6","type":"published","date":"2025-02-03T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65045728,"identity":"f940aea4-3c76-4eff-9444-a5d5ba68ceaa","added_by":"auto","created_at":"2024-09-23 04:43:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2040852,"visible":true,"origin":"","legend":"\u003cp\u003eDiffusion-programmed catalysis. \u003cstrong\u003ea\u003c/strong\u003e Schematic illustration of porous heterogeneous catalyst (MOF) in a conventional reaction set up, \u003cstrong\u003eb\u003c/strong\u003e diffusion length-dependent turn-over frequency (TOF, solid line) and geometric selectivity (dotted line) profiles for a catalytic reaction; red and green arrow indicate diffusion for A and B reactants, size A\u0026gt;B, the half spheres represent a model porous catalyst particle, \u003cstrong\u003ec\u003c/strong\u003e (i) schematic illustration of the diffusion controlled, microfluidic cross-flow reaction set up, (ii) for a catalyst with \u003cem\u003eL\u003c/em\u003e\u003csub\u003eC\u003c/sub\u003e: plausible flow-rate (\u003cem\u003eF\u003c/em\u003e) dependent TOF and geometric selectivity profiles. \u003cem\u003eS\u003c/em\u003e\u003csub\u003eP \u003c/sub\u003e= product selectivity. Following are the power laws for b and c (ii); TOF\u0026nbsp;1/\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e3 \u003c/sup\u003eand TOF\u0026nbsp;1/F\u003csup\u003e0.5\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"image1.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/749fbd7966b1c03198ca0959.png"},{"id":65044956,"identity":"cea47931-0f70-49b4-94ef-5db6b46b9a8d","added_by":"auto","created_at":"2024-09-23 04:35:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1776932,"visible":true,"origin":"","legend":"\u003cp\u003eCross-flow microfluidic catalysis. \u003cstrong\u003ea\u003c/strong\u003e chemical structure of the reactants, * = reactive –CH\u003csub\u003e2\u003c/sub\u003e group; D\u003csub\u003etBut\u003c/sub\u003e and D\u003csub\u003eEt\u003c/sub\u003e are the diffusivities of \u003cem\u003et-But\u003c/em\u003e-CA and \u003cem\u003eEt-\u003c/em\u003eCA, respectively;\u0026nbsp; \u003cstrong\u003eb\u003c/strong\u003e illustration of cross-flow microfluidic reaction using MOF thin film as catalyst, P = circulating pump, R = reactant and product chamber, yellow dotted line indicates the flow path, right: MOF catalyst thin film with controllable thickness, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e = diffusion length; \u003cstrong\u003ec\u003c/strong\u003e TOF after 24 h at 70° C for one pot reaction with smaller MOF submicron-sized particles and in cross-flow microfluidic reaction using UiO\u003csub\u003eTF1\u003c/sub\u003e (5 mL/min flow-rate), 1 mmol of benzaldehyde and nucleophiles each in 10 mL of ethanol. Conversion % is calculated using \u003csup\u003e1\u003c/sup\u003eH NMR.\u003c/p\u003e","description":"","filename":"image2.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/a00ba7f5d1dcdb548bf77fa2.png"},{"id":65044960,"identity":"c75652c5-ee31-4033-bc57-c9a453a41cb4","added_by":"auto","created_at":"2024-09-23 04:35:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654723,"visible":true,"origin":"","legend":"\u003cp\u003eTunable TOF and selectivity. \u003cstrong\u003ea\u003c/strong\u003e Catalyst film thickness dependent and \u003cstrong\u003eb\u003c/strong\u003e flow-rate (\u003cem\u003eF\u003c/em\u003e mL/min) dependent TOF and selectivity (\u003cem\u003eS\u003c/em\u003e\u003csub\u003eEt/t\u003c/sub\u003e) profiles for the \u003cem\u003eEt\u003c/em\u003e-Acr and \u003cem\u003et-But\u003c/em\u003e-Acr in cross-flow microfluidic reaction. All reactions are done at 70° C using 1 mmol of benzaldehyde and 1 mmol of nucleophiles each in 10 mL ethanol. Conversion % is calculated using \u003csup\u003e1\u003c/sup\u003eH NMR. Error-bars are calculated by carrying out three set of reactions.\u003c/p\u003e","description":"","filename":"image3.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/150f8652bea9bdd8b58cf47c.png"},{"id":65044959,"identity":"ec37be84-268f-48bc-b7be-0aa2e582faca","added_by":"auto","created_at":"2024-09-23 04:35:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":481269,"visible":true,"origin":"","legend":"\u003cp\u003eCompetitive diffusion: Yield % and selectivity of the catalysis reactions using mixture (1:1 moles/moles) of nucleophiles. Conversion % is calculated using \u003csup\u003e1\u003c/sup\u003eH NMR.\u003c/p\u003e","description":"","filename":"image4.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/c01159cd5f50f16d9b914601.png"},{"id":75402786,"identity":"827d4500-24a6-496b-be5f-a6028548b63e","added_by":"auto","created_at":"2025-02-04 08:09:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5569975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/22a4f09e-b7da-429c-ab0c-0d047b2e731d.pdf"},{"id":65044957,"identity":"35da6410-9579-42e5-b5a0-b9617dcb7191","added_by":"auto","created_at":"2024-09-23 04:35:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7195090,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5076569/v1/e607a4a1cb13197b950c2025.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Diffusion-programmed catalysis in nanoporous material","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePorous heterogeneous catalysts, like zeolites, metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) are widely researched for organic conversions.\u003csup\u003e1-3\u003c/sup\u003e The growing interest particularly in MOFs, constructed by linking metal ion/cluster with functionalized organic ligand, stems from their well-organized confined active sites, which are sterically and electronically tunable.\u003csup\u003e4-6\u003c/sup\u003e The extensive chemical diversity and predictable structure-property relationships of MOF-based catalysts have significantly expanded their applications, including in the areas such as enantiomer/size-selective catalysis, electrocatalysis, and photocatalysis.\u003csup\u003e7-15\u003c/sup\u003e Nonetheless, a persistent challenge for porous catalysts is the limitation imposed by mass transfer or diffusion.\u003csup\u003e16,17\u003c/sup\u003e Often all the catalytic sites are not accessible due to substantial diffusion barrier (surface barrier and large diffusion length), leading to lowering of conversion efficiency.\u003csup\u003e16\u003c/sup\u003e Further, poorly regulated diffusion leads to smaller geometric selectivity. Therefore the overall efficiency of porous catalysts remains underestimated. Although the diffusion mechanism\u003csup\u003e18-20\u003c/sup\u003e is well-explored in theory and simulation for porous materials, access to the highest efficiency, selective catalysis in practice remains a complex challenge. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn typical catalytic reaction, whether batch or fixed-bed types, a range of MOF particle sizes is utilized. This size heterogeneity can significantly reduce both the turnover frequency (TOF) and geometric selectivity (which is the hallmark feature of MOF catalysis\u003csup\u003e21-23\u003c/sup\u003e). The underlying reasons are as follows: in heterogeneous catalysts, active sites are located both on the surface and within the bulk. When the catalyst has a very high surface-to-volume ratio (particle size \u0026lt;10 nm), the reaction rate is governed by the surface active sites. For catalysts with a very low surface-to-volume ratio (particle size \u0026gt;10 \u0026mu;m), the very high reactant diffusion barrier limits the reaction at the surface active sites. Within the intermediate particle size range, the diffusion rate controls the reaction outcome, influencing both TOF and selectivity. This relationship is depicted in Figure 1a-b, demonstrating that for a specific catalytic reaction, there is a critical particle size or diffusion length (\u003cem\u003eL\u003csub\u003ec\u003c/sub\u003e\u003c/em\u003e) at which geometric selectivity is maximum (i.e. selectivity is controlled by diffusivity only). However, achieving this critical point in a conventional catalytic reaction appears unattainable. It is also noteworthy that maximizing geometric selectivity inherently leads to reduction in TOF; a trade-off intrinsic to any porous material-based catalytic reaction.\u003c/p\u003e\n\u003cp\u003eThere are elegant chemical pathways (defect density control,\u003csup\u003e24-27\u003c/sup\u003e postsynthetic linker modification, exchange\u003csup\u003e28-30\u003c/sup\u003e and multivariate assembly\u003csup\u003e31,32\u003c/sup\u003e) that can uplift TOF or geometric selectivity in MOF catalysis. For other types of porous catalysts (e.g. zeolites), the chemical tunability is not always straightforward. Keeping the chemical compositions constant in a MOF catalyst, we have explored the possibility of efficiency (TOF and selectivity) tuning by controlling the diffusion length only. \u0026nbsp; We realized that the primary factors are diffusion length (\u003cem\u003eL\u003csub\u003eD\u003c/sub\u003e\u003c/em\u003e) and reactant-active site residence time (collision frequency). The former can improve geometric selectivity, while the latter can boost TOF. To precisely manipulate \u003cem\u003eL\u003csub\u003eD\u003c/sub\u003e\u003c/em\u003e, we have utilized a monolithic thin film of MOF catalyst with programmable thickness, and mounted in a microfluidic cell (cross-flow) to enhance residence time. This reaction setup is illustrated in Figure 1c (i). By precisely controlling the \u003cem\u003eL\u003csub\u003eD\u003c/sub\u003e\u003c/em\u003e and residence time, for a condensation reaction we have achieved a \u0026gt;1000 fold increase in TOF and 2-fold enhanced geometric selectivity compared to a chemically equivalent batch reaction using submicron-size particles of catalyst. A straightforward control over \u003cem\u003eL\u003csub\u003eD\u003c/sub\u003e\u003c/em\u003e and residence time allows programming the diffusion of the reactants, which controls the outcome of the reaction, as depicted in Figure 1c (ii). In the following discussion we have illustrated the working principle and a general strategy to perform the diffusion-programmed catalysis, enabling access to the highest limits of TOF and geometric selectivity for any porous catalyst. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eMOF catalyzed condensation reaction\u003c/h2\u003e \u003cp\u003eThe pore window size, cavity size and chemical environment of MOFs can be engineered precisely and predictively.\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e By leveraging this chemical and structural tunability, catalytic sites within the pores can be tailored to accommodate specific reactants. This exceptional selectivity in geometry can be regulated by varying the \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e of the reactants. Literature suggests that reducing \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e (i.e., using smaller particle sizes) enhances the turnover frequency (TOF).\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e However, this increase in TOF comes at the cost of reduced geometric selectivity. To evaluate this statement, we conducted a conventional one-pot reaction using submicron-scale catalyst particles.\u003c/p\u003e \u003cp\u003eWe investigated a Knoevenagel condensation reaction catalyzed by a Lewis base (-NH\u003csub\u003e2\u003c/sub\u003e) housed within a robust MOF pore.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e The MOF of interest, UiO-66-NH\u003csub\u003e2\u003c/sub\u003e,\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e composed of Zr\u003csup\u003e4+\u003c/sup\u003e and 2-aminobenzendicarboxylic acid (NH\u003csub\u003e2\u003c/sub\u003e-bdc), was synthesized, with particle sizes\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e averaging\u0026thinsp;~\u0026thinsp;500 nm and ~\u0026thinsp;160 nm employed for the catalytic reactions (see supporting information, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4). The MOF's triangular pore window size is ~\u0026thinsp;6 \u0026Aring; and cavity diameter is ~\u0026thinsp;11 \u0026Aring; (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Ethyl cyanoacetate (\u003cem\u003eEt\u003c/em\u003e-CA; 4.5 \u0026Aring; \u0026times; 10.3 \u0026Aring;) and tert-butyl cyanoacetate (\u003cem\u003et-But\u003c/em\u003e-CA; 5.8 \u0026Aring; \u0026times; 10.3 \u0026Aring;) were chosen as the small and large nucleophiles,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e respectively, reacting with benzaldehyde (6 \u0026Aring; \u0026times; 4.3 \u0026Aring;) to form ethyl-2-cyano-3phenylacrylate (\u003cem\u003eEt\u003c/em\u003e-Acr) and ethyl-2-cyano-3phenylacrylate (\u003cem\u003et-But\u003c/em\u003e-Acr), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, S5-7). The nucleophiles' dimensions allow diffusion into the pores, albeit at varying rates due to sieving effects. Molecular dynamics (MD) simulations were employed to estimate diffusivity differences of two nucleophiles within UiO-66-NH\u003csub\u003e2\u003c/sub\u003e (see \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003emethods\u003c/span\u003e section below). Analysis of mean square displacement (MSD) profiles indicates that \u003cem\u003eEt\u003c/em\u003e-CA diffuses approximately ten times faster than \u003cem\u003et-But\u003c/em\u003e-CA (Figure S8a). Close-proximity interactions between nucleophiles and MOF pores reveal that the active -CH\u003csub\u003e2\u003c/sub\u003e group (marked with * in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) of nucleophiles predominantly interacts with the organic linker rather than the metal node (Figure S8b-c). This -CH\u003csub\u003e2\u003c/sub\u003e - NH\u003csub\u003e2\u003c/sub\u003e interaction suggests the potential for Lewis base catalyzed reactions.\u003c/p\u003e \u003cp\u003eWe anticipated the reaction rate to exceed the diffusion rate, leading to diffusion-controlled TOF. At 24 h, we achieved 42 (\u0026plusmn;\u0026thinsp;2) % and 24% conversions for the smaller and larger nucleophiles, respectively, using the ~\u0026thinsp;160 nm catalyst particles (1 mol% in ethanol, 70\u0026deg;C; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure S9-11). Under similar conditions, the ~\u0026thinsp;500 nm catalyst particles exhibited approximately 50% lower yield but improved selectivity (~\u0026thinsp;25%) for the smaller nucleophile. However, this improvement in selectivity is not deemed significant. Noteworthy, the one-pot reaction contains a large variety of particle sizes (Figure S2-3). Hence any significant change in the selectivity by varying the particle sizes is a challenge.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCross-flow microfluidic MOF catalysis\u003c/h3\u003e\n\u003cp\u003eAt this point, to regulate TOF and selectivity, we have designed a new reaction methodology. To enhance the TOF, we have done following: i) a method to improve TOF is by increasing collision frequency (reactant-active site). To achieve this, we designed a microfluidic reactor where the catalyst is supported on a solid surface (\u003cem\u003evide infra\u003c/em\u003e) and the volume of reactant solution in contact with the catalyst is 80 (\u0026plusmn;\u0026thinsp;4) \u0026micro;L. The small volume ensures that only a limited amount of reactant interacts with the entire catalyst layer at any given time, thereby enhancing reactivity. ii) The microfluidic cell is connected to a pump that circulates a larger volume of reactant solution. Consequently, both reactants and the formed products circulate in a cross-flow direction along the catalyst bed (thin film). This reaction setup resembles one-pot catalysis reactions, except that the catalyst is placed within a microfluidic cell as a thin film. iii) Reactant flow can be controlled (0.1\u0026ndash;15 mL/min) in a cross-flow direction, which helps preventing pore surface blockage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo enhance the selectivity, the catalyst is deposited as a monolithic thin film with controllable thickness (see experimental section, Figure S12). In the proposed cross-flow setup, concentration gradient is along the film thickness, and hence the thickness is \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e. This allows straightforward tuning of the \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e, unlike in the one-pot reaction (\u003cem\u003evide supra\u003c/em\u003e). The reaction setup is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Noteworthy those conventional nano/microparticles of MOFs are not suitable for this catalysis reaction. Rather, recently developed MOF thin film growth methodologies, e.g. layer-by-layer epitaxy,\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e chemical vapor deposition,\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e solution atomic layer deposition,\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e vapor assisted conversion,\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and electrochemical\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e deposition can be applied to make the catalyst layer.\u003c/p\u003e \u003cp\u003eTo execute the reaction scheme, we have synthesized UiO-66-NH\u003csub\u003e2\u003c/sub\u003e thin film (UiO\u003csub\u003eTF1\u003c/sub\u003e~120 nm thickness) on a Si/SiO\u003csub\u003e2\u003c/sub\u003e substrate at room temperature (298\u0026thinsp;\u0026plusmn;\u0026thinsp;3 K), using a drop casting methodology\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e (Figure S13). This method is suitable for controlling the film thickness with high crystalline orientation (Figure S14). Using the catalyst thin film UiO\u003csub\u003eTF1\u003c/sub\u003e we performed reactions using a similar reactant solution volume as for one-pot reactions, at 70\u0026deg;C using a flow rate of 5 mL/min (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We could realize maximum\u0026thinsp;~\u0026thinsp;96% and 35% formation for the \u003cem\u003eEt\u003c/em\u003e-Acr and \u003cem\u003et-But\u003c/em\u003e-Acr after 24 h (TOF\u0026thinsp;~\u0026thinsp;2271 and 790 h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively, Table S2, Figure S15-18). The TOF and selectivity are enhanced by ~\u0026thinsp;1300 fold and 1.5-fold, compared submicron-sized particle based catalysis. These findings, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, validate the simultaneous enhancement of TOF and selectivity through our proposed strategy. In absence of any chemical modification, these observations are unprecedented.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Subsequently, we elaborate on the underlying principles driving these radical improvements.\u003c/p\u003e\n\u003ch3\u003eDiffusion-programmed catalysis\u003c/h3\u003e\n\u003cp\u003eTo confirm that the reaction is indeed controlled by pore diffusion and that selectivity enhancement is attributable the diffusion length (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e), we have conducted reactions varying film thickness. Increasing film thickness simultaneously increases \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e and also amount of catalyst present. In the absence of diffusion control, the TOF should remain unchanged with increasing thickness. Conversely, a diffusion-regulated reaction should exhibit a quadratically decreasing TOF, assuming the nucleophiles obey Fickian diffusion (TOF \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\propto\\:\\)\u003c/span\u003e\u003c/span\u003e 1/\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). We synthesized UiO\u003csub\u003eTF2\u003c/sub\u003e and UiO\u003csub\u003eTF3\u003c/sub\u003e with thickness\u0026thinsp;~\u0026thinsp;300 (\u0026plusmn;\u0026thinsp;30) and 400 (\u0026plusmn;\u0026thinsp;50) nm, respectively (Figure S13). The film TOF vs film thickness profiles for both of the condensation products are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea (Table S2, under similar reaction conditions). The data clearly show that with increasing thickness TOF decreases and selectivity increases in a nonlinear trend. This supports the notion that the conversion is controlled by intrapore diffusion rather than being surface confined. Furthermore, it is feasible to enhance selectivity by adjusting the film thickness, i.e. \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eApart from selectivity, the sharp enhancement in TOF can be attributed to the microfluidic reaction set up. Direct evidence of this can be obtained by following experiment: A reaction of \u003cem\u003eEt\u003c/em\u003e-CA and benzaldehyde was carried out in one-pot method using the thin film catalysts UiO\u003csub\u003eTF2\u003c/sub\u003e. After 30 h the conversion was ~\u0026thinsp;50% (Table S3, Figure S19). This markedly lower conversion efficiency compared to the microfluidic reaction, confirms that the effective residence volume for the reactant solution and entire catalyst indeed enhances the TOF.\u003c/p\u003e \u003cp\u003eTo verify influence of other factors, e.g. defect density in the powder and thin film, we compared the infra-red (IR) spectra and x-ray photoelectron spectra (XPS) of those (Figure S20-21). The IR spectra indicated that the \u0026ndash;COO stretching frequencies (asymmetric and symmetric 1571and 1382 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) are similar for thin film and powder catalysts. XPS confirmed that the Zr/N ratio and nature of defects related to dangling \u0026ndash;COO are similar\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e for both the type of catalysts. We have also realized that monodispersed surface-anchored MOF particles can also enhance TOF.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e We have confirmed that the UiO\u003csub\u003eTF1\u003c/sub\u003e is a monolithic thin film, having no evident cracks or islands of crystals (Figure S13a). These above mentioned evidences support that the enhanced TOF is due to the integration of MOFs in a cross-flow microfluidic setup.\u003c/p\u003e \u003cp\u003eAfter confirming the influence of diffusion and microfluidic reaction set up on the selectivity and TOF, respectively we have performed a flow-rate dependent catalysis reaction. Flow-rate and reactant-catalyst residence time is inversely proportional. In the absence of diffusion control, it is expected that product yield % will linearly decrease with increasing flow-rate. In case of diffusion regulated process, a quadratic decrease of conversion % is expected (\u003cem\u003evide supra\u003c/em\u003e). In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cem\u003eEt\u003c/em\u003e-Acr yield % vs flow-rate profile (4h reaction at 70\u0026deg;C) exhibited a nonlinear trend (yield % \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\propto\\:\\)\u003c/span\u003e\u003c/span\u003e (1/\u003cem\u003eF\u003c/em\u003e)\u003csup\u003e0.2\u0026ndash;0.32\u003c/sup\u003e), confirming reactant diffusion limited reaction (Table S4, Figure S22-23). Moreover, \u003cem\u003et-But\u003c/em\u003e-Acr yield % also reduces nonlinearly (Table S4, Figure S24) with increasing flow rate, and we could observe highest selectivity of 11.5 at a flow-rate of 1 mL/min (\u003cem\u003eEt\u003c/em\u003e-Acr yield\u0026thinsp;~\u0026thinsp;70%). Using similar conditions (solvent, temperature and concentration) in a one-pot reaction similar TOF and selectivity is not feasible. Noteworthy, the TOF and selectivity vs flow-rate profiles in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb resemble Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, except diffusion length is replaced by flow-rate. We have shown that by straightforward tuning of flow-rate it is feasible to achieve critical diffusion path (\u003cem\u003eL\u003c/em\u003e\u003csub\u003ec\u003c/sub\u003e) which allows access to highest selectivity with higher TOF than the conventional methods.\u003c/p\u003e \u003cp\u003eIt is evident that for thickness and flow-rate based diffusion control experiments conventional Fickian diffusion is not followed, as the power law diverges from the quadratic norm. This anomalous behavior is indicative of additional factors, e.g. presence of competitive interaction with benzaldehyde and any specific chemical interaction with MOF. These factors inhibit the long-range random walk, leading to a modified TOF expressed as TOF \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\propto\\:\\)\u003c/span\u003e\u003c/span\u003e (1/\u003cem\u003eF\u003c/em\u003e)\u003csup\u003e1/(2+α)\u003c/sup\u003e, where α represents the anomalous factor.\u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above discussed experiments conclude that the TOF and selectivity can be enhanced beyond the conventional limits using the proposed scheme. A comparison of the TOF and selectivity, for similar condition (at similar temperature, solvent and time) one-pot (using 500 nm particle) and cross-flow microfluidic (using UiO\u003csub\u003eTF2\u003c/sub\u003e) reaction, confirmed\u0026thinsp;\u0026gt;\u0026thinsp;1000 and 2-fold enhancement, respectively (Table S4-5, Figure S25). In the next step, we have explored the impact of heterogeneous mixtures (i.e. mixture of \u003cem\u003eEt\u003c/em\u003e-CA and \u003cem\u003et-But\u003c/em\u003e-CA) on the reactivity. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e we have illustrated the observed TOF and selectivity for one-pot (1 mol% catalyst) and cross-flow microfluidic (4.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mol% catalyst, 0.1 mL/min) reactions (Table S6, Figure S26-S27). It is evident that competing diffusion of the reactants decreases the total conversion efficiency. However, the selectivity are found to be 5.4, higher than the one-pot reactions using large (4.3) submicron-sized catalyst particles. The improvement in the conversion efficiency is also evident; \u003cem\u003eEt\u003c/em\u003e-Acr yield % is similar for one-pot (smaller particle) and cross-flow microfluidics methods, although catalyst mol% differ by \u0026gt;\u0026thinsp;1000-fold. This confirms that even in a heterogeneous mixture of competing reactant diffusion, cross-flow microfluidic reaction using MOF catalyst monolith is superior to the state-of-the-art catalysis reactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMetal-Organic Frameworks (MOFs) are esteemed as exceptional heterogeneous catalysts among the porous materials, owing to their crystallinity, extensive chemical versatility, high surface area, and confinement effects. However, the diffusion-limited TOF and selectivity present significant challenges that continue to impede their catalytic performance. To address these issues, we have engineered a cross-flow microfluidic reaction setup and catalyst monolith, which allow precise control over reactant diffusion, thereby modulating both TOF and selectivity. This diffusion programmability is exemplified in a Knoevenagel condensation reaction, demonstrating the feasibility of simultaneously enhancing TOF and product selectivity beyond the conventional limits imposed by one-pot or batch reactors, achieving improvements by orders of magnitude. The ability to control diffusion rate allows for continuous tuning of the conversion efficiency, with experiments revealing a sub-diffusive nature of the catalytic reaction. This marks the first proof-of-concept demonstration of diffusion-programmed catalysis in porous materials, representing a significant step forward in the field.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eCharacterization techniques:\u003c/p\u003e \u003cp\u003eX-ray diffraction: The X-ray diffraction patterns of the powder and thin films were recorded using a Rigaku XDS 2000 diffractometer using nickel-filtered Cu \u003cem\u003eK\u003c/em\u003eα radiation (\u003cem\u003eλ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.5418 \u0026Aring;). Data were collected from 5 to 20\u0026deg; at room temperature (voltage 40 kV, current 200 mA). XRD was recorded in 2\u003cem\u003eθ\u003c/em\u003e/\u003cem\u003eθ\u003c/em\u003e (step size 0.01, scan rate 0.2\u0026deg;/s) geometry.\u003c/p\u003e \u003cp\u003eScanning electron microscopy (SEM): Morphology and cross-section of the thin films and powders were characterized using field emission scanning electron microscopy (FESEM), JEOL JSM-7200F instrument with a cold emission gun operating at 30 kV.\u003c/p\u003e \u003cp\u003eInfrared (IR) spectroscopy: Infrared reflection absorption spectroscopy (IRRAS) of the thin films and attenuated total reflection (ATR) absorption spectroscopy of the powder were done using the Bruker Vertex 70v instrument, with a spectral resolution of 2 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. IRRAS results were recorded in grazing incidence reflection mode at an angle of incidence 45\u003csup\u003e\u0026deg;\u003c/sup\u003e relative to the surface, under vacuum at room temperature. 1-octadecanethiol self-assembled monolayer (SAM) functionalized Au was used for background measurements.\u003c/p\u003e \u003cp\u003eX-ray photoelectron spectroscopy (XPS): Elemental detection of the powder and thin film was performed using X-ray photoelectron spectrometer (PHI versaProbe III) within an ultrahigh vacuum (1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e bar) environment. This instrument was equipped with an Al-\u003cem\u003eKα\u003c/em\u003e X-ray source and a monochromator.\u003c/p\u003e \u003cp\u003eNuclear magnetic resonance (NMR) spectroscopy: NMR spectra were recorded on a BrukerNanoBay 300 MHz NMR spectrometer. In our experiment, after the reaction, we used a syringe filter to separate the MOF powder catalyst. The entire collected solution was then concentrated and 0.4 mL of CDCl\u003csub\u003e3\u003c/sub\u003e was added to check the NMR data. For the cross-flow microfluidic catalysis, we collected the entire solution from the fluidic cell, concentrated and after adding 0.4 mL of CDCl\u003csub\u003e3\u003c/sub\u003e we checked the NMR data.\u003c/p\u003e \u003cp\u003eMolecular dynamic simulation of reactant diffusion:\u003c/p\u003e \u003cp\u003eSimulation Model: We considered a 1\u0026times;1\u0026times;1 UiO-66-NH\u003csub\u003e2\u003c/sub\u003e containing one molecule of \u003cem\u003eEt\u003c/em\u003e-CA or \u003cem\u003et-But\u003c/em\u003e-CA individually. Partial charges for the MOF atoms were obtained from earlier report,\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and other bond, angle, and dihedral parameters were modeled using OBGMX. The analyte molecules were modeled using Charmm force field generic parameters (CGenFF).\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e Simulations for each reactant molecule were conducted in the gas phase, within a rectangular box of dimensions 2.07 \u0026times; 2.07 \u0026times; 2.07 nm\u0026sup3;.\u003c/p\u003e \u003cp\u003eSimulation Method: Each simulation employed periodic boundary conditions (PBC) in all three dimensions. Long-range electrostatic interactions were managed using the particle mesh Ewald (PME) method\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e with cubic interpolation, with a 1.2 nm cutoff for short-range electrostatic interactions. The LINCS algorithm\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e was used to constrain bonds involving hydrogen atoms. The system was first energy minimized using the steepest descent algorithm, followed by stepwise equilibration over seven steps with gradual temperature increases from 50 K to 300 K, each step lasting 100 ps with a time step of 0.0005 ps. During equilibration, the average temperature was maintained using a V-rescale thermostat,\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e separately coupling the MOF and analyte molecule. The equilibrated system then underwent a 10 ns NVT production run at 300 K, also maintained by the V-rescale thermostat. All simulations were performed using GROMACS\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e version 2018.6 and repeated multiple times to ensure statistical reproducibility.\u003c/p\u003e \u003cp\u003eReactant diffusion was analyzed by calculating the mean square displacement (msd) using the 'gmx msd' tool, and the corresponding diffusion coefficient (D) was estimated. To understand the chemical interactions between reactant molecules and the MOF, pair correlation functions of active -CH\u003csub\u003e2\u003c/sub\u003e groups (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) were measured relative to specific functionalities in MOF (metal-oxo nodes, organic linkers, and -NH\u003csub\u003e2\u003c/sub\u003e group of the organic linkers).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eWe acknowledge the intramural funding at TIFR Hyderabad from the Department of Atomic Energy (DAE), India, under Project Identification Number RTI 4007.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePascanu V, Gonz\u0026aacute;lez Miera G, Inge AK (2019) Mart\u0026iacute;n-Matute, B. Metal\u0026ndash;Organic Frameworks as Catalysts for Organic Synthesis: A Critical Perspective. 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SoftwareX 1\u0026ndash;2 GROMACS:19\u0026ndash;25\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5076569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5076569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the realm of heterogeneous catalysis, the diffusion of reactants into catalytically active sites stands as a pivotal determinant influencing both turnover frequency and geometric selectivity in product formation. While accelerated diffusion of reactants can elevate reaction rates, it often entails a compromise in geometric selectivity. Porous catalysts, including metal-organic and covalent organic frameworks, confront formidable obstacles in regulating reactant diffusion rates. Consequently, the chemical functionality of the catalysts typically governs turnover frequency and selectivity. This study presents an approach harnessing diffusion length to achieve improved selectivity and manipulation of reactant-active site residence time at active sites to augment reaction kinetics. Through the deployment of a thin film composed of a porous metal-organic framework catalyst, we illustrate how programming reactant diffusion within a cross-flow microfluidic catalytic reactor can concurrently amplify turnover frequency (exceeding 1000-fold) and enhance geometric selectivity (~\u0026thinsp;2-fold) relative to conventional nano/microcrystals of catalyst in one-pot reactor. This diffusion-programed strategy represents a robust solution to surmount the constraints imposed by bulk nano/microcrystals of catalysts, marking advancement in the design of porous catalyst-driven organic reactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Diffusion-programmed catalysis in nanoporous material","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-23 04:35:15","doi":"10.21203/rs.3.rs-5076569/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c84c351-a721-40a0-ae80-59d05bd0ca85","owner":[],"postedDate":"September 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37900120,"name":"Physical sciences/Materials science/Materials for energy and catalysis/Metal\u0026#x2013;organic frameworks"},{"id":37900121,"name":"Physical sciences/Materials science/Materials for energy and catalysis/Porous materials"}],"tags":[],"updatedAt":"2025-02-04T08:09:08+00:00","versionOfRecord":{"articleIdentity":"rs-5076569","link":"https://doi.org/10.1038/s41467-025-56575-6","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-02-03 05:00:00","publishedOnDateReadable":"February 3rd, 2025"},"versionCreatedAt":"2024-09-23 04:35:15","video":"","vorDoi":"10.1038/s41467-025-56575-6","vorDoiUrl":"https://doi.org/10.1038/s41467-025-56575-6","workflowStages":[]},"version":"v1","identity":"rs-5076569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5076569","identity":"rs-5076569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}