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Effects of oxygen functionalities on hydrous hydrazine decomposition over carbonaceous materials

Silvio Bellomi a, Ilaria Barlocco a, Simone Tumiati b, Patrizia Fumagalli b, Nikolaos Dimitratos cd, Alberto Roldan *e and Alberto Villa *a
aDipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, I-20133, Milano, Italy. E-mail: alberto.villa@unimi.it
bDipartimento di Scienze della Terra Ardito Desio, Università degli Studi di Milano, via Mangiagalli 34, Milano I-20133, Italy
cDipartimento di Chimica Industriale “Toso Montanari”, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 4, Bologna 40126, Italy
dCenter for Chemical Catalysis-C3, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 4, Bologna 40136, Italy
eCardiff Catalysis Institute, School of Chemistry, Cardiff University, Main Building, Park Place, CF10 3AT, Cardiff, UK. E-mail: RoldanMartinezA@cardiff.ac.uk

Received 20th July 2023 , Accepted 17th September 2023

First published on 26th September 2023


Abstract

Metal-free heterogeneous catalysis is promising in the context of H2 generation. Therefore, establishing structure–activity relationships is a crucial issue to improve the development of more efficient catalysts. Herein, to evaluate the reactivity of the oxygen functionalities in carbonaceous materials, commercial functionalized pyrolytically stripped carbon nanofibers (CNFs) were used as catalysts in the liquid-phase hydrous hydrazine decomposition process and its activity was compared to that of a pristine CNF material. Different oxygenated groups were inserted by treating CNFs with hydrogen peroxide for 1 h (O1-H2O2) and HNO3 for 1 h (O1-HNO3) and 6 h (O6-HNO3). An increase in activity was observed as a function of the oxidizing agent strength (HNO3 > H2O2) and the functionalization time (6 h > 1 h). A thorough characterization of the catalysts demonstrated that the activity could be directly correlated with the oxygen content (O6-HNO3 > O1-HNO3 > O1-H2O2 > CNFs) and pointed out the active sites for the reaction at carbon–oxygen double bond groups (C[double bond, length as m-dash]O and COOH). Systematic DFT calculations supported rationalization of the experimental kinetic trends with respect to each oxygen group (C[double bond, length as m-dash]O, C–O–C, C–OH and COOH).


Introduction

The search for sustainable chemical processes is crucial for achieving a resilient society. Alongside this, the global energetic demand is steadily increasing, raising the necessity for higher energy generation capacities, better management and efficiency of generated energy, and lower dependence on non-renewable fossil fuels.1

Molecular hydrogen is recognized as one of the most promising clean energy sources due to its high intrinsic energy density and innocuous by-products upon utilization. Hydrogen can be produced through several methods, including photocatalytic and electro-catalytic systems, steam reforming and biomass-, coal- or natural gas-based processes.1 Even though the benefits of hydrogen are apparent, its direct usage is limited by the lack of economically sustainable and safe storage and transportation technologies.2 Typically, storage procedures require pressurized tanks, both for gaseous or liquified hydrogen, whose applicability is restricted by low efficiencies and high costs.3,4

Liquid Hydrogen Carriers (LHCs) are easily storable and transportable chemical compounds with a high intrinsic hydrogen content, which can be released chemically, desirably under mild conditions. Many LHCs exist, but the need to reduce CO2 emissions has led to a focus on nitrogen–hydrogen compounds. N2H4 possesses a hydrogen content of 12.5 wt%.5 Nonetheless, given its intrinsic hypergolic nature, i.e., serious risk of explosions in the presence of certain metals,6 it is handled in the hydrated form, hydrous hydrazine (N2H4·H2O, 8 wt% hydrogen content).7,8 N2H4·H2O is reported to decompose catalytically through two different pathways:9

 
N2H4 → N2(g) + 2H2(g) ΔH° = −95.4 kJ mol−1(1)
 
3N2H4 → N2(g) + 4NH3(g) ΔH° = −157.0 kJ mol−1(2)

The first reaction (eqn (1)) represents the complete reforming pathway, where molecular hydrogen and nitrogen are produced. The second path (eqn (2)) produces nitrogen and ammonia and is the thermodynamically favoured process.

Several studies focused on using noble10,11 and non-noble12,13 metal-based catalysts to promote hydrazine decomposition. These investigations highlighted a dependence of the reaction selectivity upon the catalyst's nature and the reaction parameters, e.g., pressure, temperature, and stirring.11,14–17 Despite the catalytic activity presented, the carbon fingerprint and scalability of the metal catalysts are questionable due to their mining and manufacturing methods and scarcity. Therefore, it is advised to design new metal-free catalysts for a sustainable future.18

Carbon-based materials are attracting significant attention as catalysts due to their high tunability. Facile changes in their lattice structure allow significant variations of, for instance, surface area, porosity, functional groups and topological defects.19 Of particular interest are those metal-free carbo-catalysts that successfully generated hydrogen in the liquid phase from different substrates, including alkenes, alkanes and formic acid.20,21

Several works focused on the hydrazine-mediated reduction of different target substrates.22–24 Remarkable results were obtained for the reduction of nitroarenes performed over carbo-catalysts.25 Fujita and co-workers26 complemented these results unveiling the effect of N and O functionalities. The introduction of dopants induced changes in the surface properties of the carbons, enhancing the catalytic properties of the carbo-catalysts, i.e., conversions and selectivity of the target substrates.

Previous investigations on the activity of carbonaceous materials’ intrinsic defects for hydrous hydrazine decomposition unveiled fully that pyrolytically stripped carbon nanofibers (CNFs) exhibited the best performances among the tested carbo-catalysts thanks to a high degree of intrinsic defects, resulting in complete conversion of hydrous hydrazine up to 6 h of reaction with 89% H2 selectivity.27 Despite previous works and to the best of our knowledge, the literature lacks investigations rationalizing the effects of oxygen functionalities in metal-free carbonaceous materials for the decomposition of hydrous hydrazine.

The work presented here focuses on the liquid-phase oxygen functionalization of CNFs through different procedures and unveils their role in hydrous hydrazine decomposition. The nature of oxygen groups was modulated by employing different oxidizing agents (H2O2 and HNO3) at variable functionalization times (1 and 6 h), resulting in O1-H2O2, O1-HNO3, and O6-HNO3. The functionalized carbo-catalysts were tested as catalysts under mild conditions, and their activity was compared with that of the pristine CNFs. To understand the effect of the functionalization procedures on the carbonaceous materials, fresh and spent catalysts were thoroughly characterized through ICP, Raman and XPS in combination with computational techniques.

Experimental

Materials and chemicals

Pyrolytically stripped carbon nanofibers (CNFs) were purchased from the Applied Science Company. Hydrous hydrazine (N2H4·H2O, 98%), sodium hydroxide (NaOH, ≥98%), nitric acid (HNO3, 65%) and hydrogen peroxide (H2O2, 30%) were acquired from Sigma-Aldrich.

Catalyst synthesis

In a typical functionalization procedure, 1 g CNFs were dispersed in 200 ml of the desired oxidizing agent (HNO3 65% or H2O2 30%) in a 500 ml round bottom flask. Two different oxidizing agents were employed, namely HNO3 and H2O2. For the HNO3 treatment, a 353 K temperature and an 800 rpm stirring rate were employed, and the mixture was left for the desired time (1 or 6 h), resulting in O1- and O6-HNO3, respectively. Differently, for the H2O2 treatment, the functionalization was performed at RT and with a stirring rate of 800 rpm for 1 h, leading to O1-H2O2. A longer functionalization time with H2O2 (6 h) did not produce significantly different kinetic results from the 1 h functionalized carbons; thus, we excluded O6-H2O2 from the proposed investigation.

Catalytic tests

Liquid phase N2H4·H2O decomposition was performed at a constant reaction temperature of 323 K using a 50 mL three-necked round bottom flask, with one of the flask's necks connected to a burette for the volumetric analysis of the gaseous products. Typically, the desired amount of catalyst (hydrous hydrazine/catalyst wt. ratio 16/1) was added in the reactor in the presence of 16.0 mL of a 0.5 M NaOH aqueous solution, and equilibrated at a stirring rate of 1400 rpm. Then, 600 μL of a 3.3 M aqueous solution of hydrous hydrazine was injected. Hydrous hydrazine conversion was analysed through a Jasco V-730 spectrophotometer, using a 1 cm quartz cell, based on the quantitative reaction of N2H4·H2O with 4-dimethylaminobenzaldehyde (4-DMAB) in dilute hydrochloric acid (HCl), resulting in a p-quinone structure that strongly absorbs radiation at 456 nm (Fig. 1).28 The volume of gas produced was measured using the water displacement method. In a typical experiment, the reactor was connected with a tube fitted under a flipped burette, filled with water, and placed in a beaker containing H2O. During the evolution, the gases were allowed to pass into a 0.05 M HCl aqueous solution to ensure the removal of NH3, if any. Therefore, we quantified the evolved gaseous products (H2 and N2) from the volume of water displaced within the burette.27
image file: d3dt02310a-f1.tif
Fig. 1 Reaction of 4-DMAB and hydrous hydrazine to obtain the UV-Vis detectable p-quinone.

Catalyst characterization

Functionalized carbon materials were characterized by Raman spectroscopy and X-ray Photoelectron Spectroscopy (XPS). Raman spectroscopy was performed at the Department of Earth Sciences – University of Milan, using a Horiba LabRam HR Evolution micro-Raman spectrometer equipped with a green solid-state laser (532 nm) focused through a 100× objective, giving a spatial resolution of approximately 1 μm. The micro-Raman system was set with a 600 lines per mm grating and a hole of size 300 μm; the spectrum was collected with a final laser power of about 0.05 mW. The sample surface was measured through a hand-held power meter. Spectra were calibrated using the 520.7 cm−1 line of a silicon wafer. The sample was scanned at an exposure time of 300 s, and two accumulations were performed, giving a spectrum. XPS measurements were performed with a Thermo Scientific K-alpha+ spectrometer. The samples were analysed using a monochromatic Al X-ray source operating at 72 W, with the signal averaged over an oval-shaped area of 600 × 400 μm. Data were recorded at 150 eV for survey scans and 40 eV for high-resolution (HR) scans with a 1 eV and 0.1 eV step size, respectively. CASAXPS (v2.3.17 PR1.1) was used to analyse the XPS raw data. The C1s and O1s XPS core levels were fitted in agreement with models previously reported in the literature.29 The presence of possible residual metal was analysed by inductively coupled plasma optical emission spectroscopy (ICP-OES) using a PerkinElmer Optima 8000 emission spectrometer.

Computational details

Plane-wave density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package (VASP).30,31 A generalized gradient in the revised Perdew–Burke–Ernzerhof approximation functional (rPBE)32 was employed to evaluate the correlation-exchange electronic contributions, with a kinetic energy cutoff of 500 eV chosen for the expansion of the Kohn–Sham valence state plane-waves.33 rPBE is found to be efficient in modelling surface adsorption processes.32 In addition, the VASP implementation of the rPBE functional improves the previous revPBE implementation,34 fulfilling the Lieb–Oxford criterion globally by construction without any additional fitted parameter. All the calculations included the long-range dispersion correction approach by the Grimme DFT-D3 methods.35,36 The implicit polarizable continuum solvation model, as implemented in VASPsol,37,38 was included in all the calculations. The optimization thresholds were 10–5 eV and 0.02 eV Å−1 for electronic and ionic force relaxation. For the Brillouin zone (BZ) sampling, a Γ-centred k-point mesh generated through the Monkhorst–Pack method of dimensions 5 × 5 × 1 was employed.39 A first-order Methfessel–Paxton method was used with an energetic width value of 0.1 eV to ease the BZ sampling and improve the wavefunction convergence. All carbon-based materials were modelled starting from a single layer slab of a 6 × 6 pristine graphene supercell, where a single vacancy (SV) was introduced and functionalized with different O-containing groups, namely carbonylic (C[double bond, length as m-dash]O), esteric (C–O–C), hydroxylic (C–OH) and carboxylic (COOH) moieties (Fig. S1), in agreement with the XPS fitting model employed.29 The supercell is in a hexagonal crystalline system with unit cell vectors a and b lying in the surface plane (γ = 120°) and perpendicular to the c axis. Both a and b were optimized at 2.46 Å, in good agreement with the experimental values obtained by HR-TEM for carbon materials.40 A polarizable continuum dielectric bath of ∼16 Å perpendicular to the C-surface was introduced to avoid spurious periodic interactions. The adsorption energy (Eads) was defined as the difference between the combined system and the isolated species. Gibbs energies of adsorption (Gads) were evaluated by adding zero-point energy corrections and entropy terms (details can be found in the ESI).41 The Gads values were calculated in the harmonic approximation, while the entropy of hydrous hydrazine was obtained from the international tables.42 The entropies of solid-state species were considered negligible.43 All thermal corrections were made at the reaction temperature (323 K).

Results and discussion

Structure

The effect of oxygen functionalities on the hydrous hydrazine decomposition was investigated by modulating the nature of the oxidizing agent and the functionalization time on pristine carbon nanofibers (CNFs). As reported in the literature, a strong oxidizing agent such as nitric acid (HNO3) can preferentially introduce carboxylic/carbonylic (C[double bond, length as m-dash]O/COOH) species proportional to the functionalization time, whereas a milder oxidizing strength compound, e.g., hydrogen peroxide (H2O2), increases the hydroxylic/esteric (C–OH/–C–O–C) group content.44

All the catalysts were thoroughly characterized through ICP-OES, Raman spectroscopy and XPS to disclose the catalysts’ structure–activity relationship. The results from the ICP-OES analysis confirmed the absence of metal impurities in the catalysis of the hydrous hydrazine decomposition.10,45 Raman spectroscopy evaluated the effect of the functionalization procedures on the catalyst morphology.27 Commonly, carbon materials present two characteristic bands at around 1600 cm−1 (G band) and 1350 cm−1 (D band), and their intensity ratio (ID/IG) is commonly used as a descriptor of the defect density. In fact, the G band intensity correlates with structurally ordered graphite domains. The D band is symmetry forbidden and only allowed in structural defects or in-plane substitutional heteroatoms.46 The comparable ID/IG ratios between the functionalized carbons and CNFs allowed us to infer that the functionalization procedures did not induce modification in the carbon framework, i.e., substitutional heteroatoms, Fig. S2 and Table S1, excluding the influence of additional intrinsic defects for the hydrous hydrazine decomposition. XPS provided information on the oxygen functionalities from analyses of the C1s and O1s core levels. The treatment with 6 h in HNO3 introduced the highest quantity of oxygen groups (O/C = 0.157, O6-HNO3), followed by the treatment for 1 h in HNO3 (O/C = 0.141, O1-HNO3) and 1 h in H2O2 (O/C = 0.141, O1-H2O2). All the procedures increased the number of oxygen functionalities with respect to the pristine material (O/C = 0.122, CNFs). The results are summarised in Table S1.

We carried out a quantitative assessment of surface oxygen through high-resolution (HR) XPS analyses, focused on the O1s and C1s core levels to obtain more insights into the type of oxygen and surface content of each group.47–49 Based on previously reported models, five peaks were considered for the O1s region as a function of their binding energies (BE).29 The peaks at 531.1–531.8 eV can be assigned to carbon–oxygen double bonds (C[double bond, length as m-dash]O), those at 532.4–532.7 eV to carbon–oxygen ether-like single bonds (C–O–C), and those at 533.3–533.6 eV to carbon–oxygen bonds in hydroxylic moieties (C–OH). Carboxylic moieties (COOH) are in the BE region of 532.0–533.0 eV, presenting an intermediate character between single and double bonds. A broad peak at about 535.2 eV is assigned to residual adsorbed water on the catalyst's surface. The results of the O1s core level fitting are summarised in Fig. 2. The C1s core levels, Fig. S4 and Table S3, agree with the O1s data.


image file: d3dt02310a-f2.tif
Fig. 2 HR-XPS analysis of the O1s region and speciation of the oxygen-containing functional groups for the different catalysts.

Functionalizing CNFs with H2O2 for 1 h (O1-H2O2) increased the ether-like (C–O–C) and hydroxylic (C–OH) groups to 23.1 and 18.2% compared to 19.5 and 14.3% in the initial CNFs, Fig. 2 and Table S2. Differently, the HNO3 functionalization of CNFs, at both 1 h (O1-HNO3) and 6 h (O6-HNO3), led to the preferential formation of carbonylic (C[double bond, length as m-dash]O, 28.6 and 37.6% vs. 26.6% in the original CNFs) and carboxylic groups (COOH, 37.8 and 44.4% vs. 32.2% in CNFs). These results align with the higher oxidation strength of HNO3 than that of H2O2, Table S2, and with previous reports.44,50

Activity

The hydrous hydrazine decomposition on each catalyst was studied in a batch reactor under mild conditions (323 K and 1 atm). The conversions were quantified through the UV-Vis analysis of hydrous hydrazine quantitatively reacted with a colorimetric reagent (Fig. 1). At the same time, the selectivity towards H2 production was calculated via the water displacement method when gas evolution reached a plateau, i.e., the end of the decomposition reaction. The kinetic results are represented in Fig. 3, showing that at 150 minutes of reaction, O6-HNO3 resulted in the highest catalytic conversion (89%), followed by O1-HNO3 (65%), O1-H2O2 (52%), and CNFs (45%). The O6-HNO3 sample completed the conversion within 210 minutes of reaction, much faster than other catalysts (360 minutes). The enhanced activity in the presence of oxygen functionalities agrees with previous works on functionalized carbons for the hydrazine-mediated H transfer to nitroarenes.25,26 Differently, we observed a decrease in H2 production concerning the oxidizing agent strength and functionalization time (O6-HNO3, 8% < O1-HNO3, 14% < O1-H2O2, 35% < CNFs, 89%). Therefore, we postulate that the reported enhanced hydrogen transfer in the presence of oxygen functionalities25,26 is due to the hydrous hydrazine decomposition intermediates, capable of transferring H more readily to the reduction targets.
image file: d3dt02310a-f3.tif
Fig. 3 Conversion trend in the hydrazine decomposition reaction for the different catalysts: CNFs (black), O1-HNO3 (blue), O6-HNO3 (green) and O1-H2O2 (red). All the tests were performed at 323 K and 1400 rpm, using 600 μL of a 3.3 M hydrazine solution in 16 mL of 0.5 M NaOH and a N2H4·H2O[thin space (1/6-em)]: [thin space (1/6-em)]catalyst weight ratio of 16[thin space (1/6-em)]:[thin space (1/6-em)]1.

Structure–activity relationship

The oxygen content is linearly correlated with the catalytic activity (89%, O6-HNO3 > 65%, O1-HNO3 > 52%, O1-H2O2 > 52%, CNFs). Thus, confirming the active role of the oxygen groups in the hydrous hydrazine decomposition, Fig. 4a. However, the greater the number of oxygen functionalities, the lower the selectivity towards H2 (8%, O6-HNO3 < 14%, O1-HNO3 < 35%, O1-H2O2 < 89%, CNFs), although no linear correlation has been observed, Fig. 4b. Table S1 summarises the structural features (O/C ratio from XPS and ID/IG from Raman) of the different catalysts considered and catalytic conversions at 150 minutes and H2 selectivity at the end of the reaction. We plotted the N2H4 conversion as a function of the total surface content of each functional group in Fig. 5 to identify the active site for the reaction. A positive correlation was found between the conversion and the concentrations of C[double bond, length as m-dash]O (R2 = 0.9708) and COOH (R2 = 0.9883). On the other hand, a content increase of C–O–C (R2 = 0.8726) and C–OH (R2 = 0.9117) decreases the catalytic performances. These results lead to the conclusion that carbon–oxygen double bond groups, i.e., C[double bond, length as m-dash]O and COOH, are the active sites for the hydrous hydrazine decomposition reaction.
image file: d3dt02310a-f4.tif
Fig. 4 The trends of (a) conversion at 150′ and (b) H2 selectivity concerning the oxygen content (O/C ratio) derived from XPS analyses. Insets are the catalyst labels and R2 factor of the linear fit.

image file: d3dt02310a-f5.tif
Fig. 5 Trends of conversion with respect to the total surface content of (a) C[double bond, length as m-dash]O, (b) C–O–C, (c) C–OH and (d) COOH, as quantified from the XPS analysis. Insets are the catalyst labels and R2 factor of the linear fit.

Atomistic modeling

Systematic DFT simulations were employed to unravel the role of oxygen functionalities at the atomistic scale and rationalize the catalytic performances and kinetic trends in relation to the surface species. Fig. S1 shows the four different graphitic surfaces modelled as derived from XPS, i.e., C[double bond, length as m-dash]O, C–O–C, C–OH and COOH. All the structures were optimized from relaxed graphene layers with a single vacancy to which the desired functional group was attached.51

Previous works on hydrazine highlighted the cis configuration of N2H4 as the most favorable for adsorption processes.12,27,52 Therefore, cis-N2H4 was brought close to the functional group on the surface, so that the N atoms would be able to interact properly with the dangling bonds The most favourable adsorptions (Fig. 6 and S5) led to trends as a function of the oxygenated species: C–OH (−0.82 eV) < C[double bond, length as m-dash]O (−1.39 eV) < COOH (−1.34 eV) < C–O–C (−1.62 eV). Fig. 6 shows that the N2H4 interaction with C[double bond, length as m-dash]O, C–OH and COOH promoted the N–N dissociation, leading to two NH2 groups saturating nearby C-dangling bonds. In addition, for all the surfaces, anti and gauche configurations were tested, favoring the interaction of the dangling bonds with the N–H moieties, but the process was found to be endothermic for all the structures.


image file: d3dt02310a-f6.tif
Fig. 6 Top and side views of the most favourable adsorption configurations upon interaction with N2H4: (a) C[double bond, length as m-dash]O, (b) C–O–C, (c) C–OH and (d) COOH. The carbon atom is labelled brown, oxygen in red and hydrogen in white. Inset are the Gibbs adsorption energies (Gads).

The initial exploration of the N2H4 interaction with the functionalized surface, in agreement with the previous experimental results, indicates Gibbs energies of ∼−1.3/1.4 eV to be neither too strong nor too weak to promote the N2H4 decomposition.53 The strong N2H4 molecular adsorption on C–O–C sites may lead to surface poisoning, a hypothesis confirmed by the systematic decrease of C–O–C moieties observed in the O1s core level of the spent catalysts, Table S4. The orientation of the adsorbates and the functional C[double bond, length as m-dash]O and COOH groups also points to a possible H-transfer and enolate formation as part of the catalytic cycle, which C–OH cannot undertake. The exothermic adsorption on the active sites (C[double bond, length as m-dash]O and COOH) agrees with the experimental enhanced catalytic activity observed with increasing oxygen content. In addition, we previously demonstrated that the scission of the N–N bond in the first adsorption step of hydrazine favours NH3 formation (eqn (2))27 and previous works on hydrazine mediated H-transfer highlighted enhanced reduction of target substrates in the presence of oxygenated carbonaceous materials.25,26 Therefore, we explain the selectivity results of the active sites as a combined N–N dissociation and preferential H-transfer, suppressing H2 generation as a whole.

Conclusions

A combination of computational and experimental studies was employed to elucidate the role of oxygen functionalities in carbonaceous materials for the hydrous hydrazine decomposition reaction. Three different oxygen functionalized catalysts were synthesized (O1-H2O2, O1-HNO3 and O6-HNO3) using different oxidizing agents (HNO3 and H2O2) and variable times (1 and 6 h). These samples were compared to the pristine nanofibers. An enhanced catalytic activity (89%, O6-HNO3 > 65%, O1-HNO3 > 52%, O1-H2O2 > 52%, CNFs) and decreased H2 production (8%, O6-HNO3 < 14%, O1-HNO3 < 35%, O1-H2O2 < 89%, CNFs) were observed concerning the oxidizing agents’ strength and functionalization time. A thorough characterization of the catalyst through Raman spectroscopy and XPS unveiled a positive linear correlation between the oxygen content and the catalytic activity (R2 = 0.9459). In contrast, a negative trend was observed for hydrogen production, although there is no linear correlation (R2 = 0.7129). A targeted analysis of O1s and C1s core levels and kinetic data assigned carbon–oxygen double bond groups (COOH and C[double bond, length as m-dash]O) as active sites. The adsorption of hydrazine on graphitic surfaces resembling those derived from XPS was simulated using DFT, resulting in a preferential N–N breakage at the active sites (C[double bond, length as m-dash]O and COOH). These groups may form enolate intermediates, favouring the NH3 and explaining the experimental trends in H2 selectivity.

Author contributions

Conceptualization, A. V.; validation, A. V. and A. R.; formal analysis, investigation and writing – original draft preparation, S. B. Writing – review and editing, A. R., I. B., P. F., and S.T. All authors have read and agreed to the published version of the manuscript.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This research is part of the project “One Health Action Hub: University Task Force for the resilience of territorial ecosystems”, supported by Università degli Studi di Milano – PSR 2021 – GSA. We acknowledge the computing time at the facilities of Supercomputing Wales and the Advanced Research Computing Cardiff (ARCCA) at Cardiff University.

References

  1. T. N. Veziroǧlu and S. Şahin, Energy Convers. Manage., 2008, 49, 1820–1831 CrossRef .
  2. P. Nikolaidis and A. Poullikkas, Renewable Sustainable Energy Rev., 2017, 67, 597–611 CrossRef CAS .
  3. H. T. Hwang and A. Varma, Curr. Opin. Chem. Eng., 2014, 5, 42–48 CrossRef .
  4. D. Pukazhselvan, V. Kumar and S. K. Singh, Nano Energy, 2012, 1, 566–589 CrossRef CAS .
  5. J. K. Norskov and C. H. Christensen, Science, 2006, 312, 1322–1323 CrossRef CAS PubMed .
  6. Z. Zhang, S. Zhang, Q. Yao, X. Chen and Z.-H. Lu, Inorg. Chem., 2017, 56, 11938–11945 CrossRef CAS PubMed .
  7. S. J. Cho, J. Lee, Y. S. Lee and D. P. Kim, Catal. Lett., 2006, 109, 181–186 CrossRef CAS .
  8. A. K. Singh, M. Yadav, K. Aranishi and Q. Xu, Int. J. Hydrogen Energy, 2012, 37, 18915–18919 CrossRef CAS .
  9. P.-X. Zhang, Y.-G. Wang, Y.-Q. Huang, T. Zhang, G.-S. Wu and J. Li, Catal. Today, 2011, 165, 80–88 CrossRef CAS .
  10. S. Bellomi, I. Barlocco, X. Chen, J. J. Delgado, R. Arrigo, N. Dimitratos, A. Roldan and A. Villa, Phys. Chem. Chem. Phys., 2023, 25, 1081–1095 RSC .
  11. D. Motta, I. Barlocco, S. Bellomi, A. Villa and N. Dimitratos, Nanomaterials, 2021, 11, 1340 CrossRef CAS PubMed .
  12. S. S. Tafreshi, A. Roldan and N. H. de Leeuw, Phys. Chem. Chem. Phys., 2015, 17, 21533–21546 RSC .
  13. W. Kang, H. Guo and A. Varma, Appl. Catal., B, 2019, 249, 54–62 CrossRef CAS .
  14. L. Zhou, X. Luo, L. Xu, C. Wan and M. Ye, Catalysts, 2020, 10, 8 Search PubMed .
  15. L. He, B. Liang, L. Li, X. Yang, Y. Huang, A. Wang, X. Wang and T. Zhang, ACS Catal., 2015, 5, 1623–1628 CrossRef CAS .
  16. K. S. Sanjay, X. B. Zhang and Q. Xu, J. Am. Chem. Soc., 2009, 131, 9894–9895 CrossRef PubMed .
  17. X. Lu, S. Francis, D. Motta, N. Dimitratos and A. Roldan, Phys. Chem. Chem. Phys., 2020, 22, 3883–3896 RSC .
  18. N. Gupta, O. Khavryuchenko, A. Villa and D. Su, ChemSusChem, 2017, 10, 3030–3034 CrossRef CAS PubMed .
  19. M. S. Shafeeyan, W. M. A. W. Daud, A. Houshmand and A. Arami-Niya, Appl. Surf. Sci., 2011, 257, 3936–3942 CrossRef CAS .
  20. I. Barlocco, S. Capelli, X. Lu, S. Tumiati, N. Dimitratos, A. Roldan and A. Villa, Nanoscale, 2020, 12, 22768–22777 RSC .
  21. J. Zhang, D. S. Su, R. Blume, R. Schlögl, R. Wang, X. Yang and A. Gajović, Angew. Chem., Int. Ed., 2010, 49, 8640–8644 CrossRef CAS PubMed .
  22. T. Hirashima and O. Manabe, Chem. Lett., 1975, 4, 259–260 CrossRef .
  23. G. R. Srinivasa, K. Abiraj and D. C. Gowda, Indian J. Chem. - Sect. B Org. Med. Chem., 2003, 42, 2885–2887 Search PubMed .
  24. D. Cantillo, M. M. Moghaddam and C. O. Kappe, J. Org. Chem., 2013, 78, 4530–4542 CrossRef CAS PubMed .
  25. J. W. Larsen, M. Freund, K. Y. Kim, M. Sidovar and J. L. Stuart, Carbon, 2000, 38, 655–661 CrossRef CAS .
  26. S. Fujita, H. Watanabe, A. Katagiri, H. Yoshida and M. Arai, J. Mol. Catal. A: Chem., 2014, 393, 257–262 CrossRef CAS .
  27. I. Barlocco, S. Bellomi, S. Tumiati, P. Fumagalli, N. Dimitratos, A. Roldan and A. Villa, Phys. Chem. Chem. Phys., 2022, 24, 3017–3029 RSC .
  28. C. Gojon and B. Dureault, J. Nucl. Sci. Technol., 1996, 33, 731–735 CrossRef CAS .
  29. R. Arrigo, M. Hävecker, S. Wrabetz, R. Blume, M. Lerch, J. McGregor, E. P. J. Parrott, J. A. Zeitler, L. F. Gladden, A. Knop-Gericke, R. Schlögl and D. S. Su, J. Am. Chem. Soc., 2010, 132, 9616–9630 CrossRef CAS PubMed .
  30. G. Kresse and J. Furthmüller, Phys. Rev. B: Condens. Matter Mater. Phys., 1996, 54, 11169–11186 CrossRef CAS PubMed .
  31. G. Kresse, J. Non-Cryst. Solids, 1995, 192–193, 222–229 CrossRef .
  32. B. Hammer, L. B. Hansen and J. K. Nørskov, Phys. Rev. B: Condens. Matter Mater. Phys., 1999, 59, 7413–7421 CrossRef .
  33. N. D. Mermin, Phys. Rev., 1965, 137, 1–3 CrossRef .
  34. Y. Zhang and W. Yang, Phys. Rev. Lett., 1998, 80, 890 CrossRef CAS .
  35. R. Sure, J. Antony and S. Grimme, J. Phys. Chem. B, 2014, 118, 3431–3440 CrossRef CAS PubMed .
  36. S. Ehrlich, J. Moellmann, W. Reckien, T. Bredow and S. Grimme, ChemPhysChem, 2011, 12, 3414–3420 CrossRef CAS PubMed .
  37. R. Sundararaman and K. Schwarz, J. Chem. Phys., 2017, 146(8) DOI:10.1063/1.4976971 .
  38. K. Mathew, R. Sundararaman, K. Letchworth-Weaver, T. A. Arias and R. G. Hennig, J. Chem. Phys., 2014, 140(8) DOI:10.1063/1.4865107 .
  39. J. D. Pack and H. J. Monkhorst, Phys. Rev. B: Solid State, 1977, 16, 1748–1749 CrossRef .
  40. Y. W. Tan, H. L. Stormer, P. Kim, K. S. Novoselov, M. L. Cohen, S. G. Louie, X. Wang, L. Zhang, S. Lee, H. Dai, Y. Kobayashi, K. Fukui, M. Fujita, G. Dresselhaus, M. S. Dresselhaus, M. A. Pimenta, B. R. A. Neves, A. Jorio, Y. Zhang, M. Mailman, P. M. Ajayan, S. K. Nayak, C. H. Park, Y. W. Son, S. P. Lu, S. Piscanec, A. C. Ferrari, G. Dobrik, P. Lambin, A. Oberlin, T. Solid, K. Suenaga, S. Iijima, P. Hermet, V. Meunier, L. Henrard, D. Gunlycke, C. T. White, S. Chen, B. I. Yakobson and S. Gradecak, Science, 2009, 323, 1705–1708 CrossRef PubMed .
  41. I. Barlocco, L. A. Cipriano, G. Di Liberto and G. Pacchioni, J. Catal., 2023, 417, 351–359 CrossRef CAS .
  42. M. W. Chase and N. I. S. O. (US), NIST-JANAF thermochemical tables, American Chemical Society Washington, DC, 1998, vol. 9 Search PubMed .
  43. L. A. Cipriano, G. Di Liberto and G. Pacchioni, ACS Catal., 2022, 12, 11682–11691 CrossRef CAS .
  44. S. Sengodan, R. Lan, J. Humphreys, D. Du, W. Xu, H. Wang and S. Tao, Renewable Sustainable Energy Rev., 2018, 82, 761–780 CrossRef CAS .
  45. S. K. Singh, A. K. Singh, K. Aranishi and Q. Xu, J. Am. Chem. Soc., 2011, 133, 19638–19641 CrossRef CAS PubMed .
  46. F. Tuinstra and J. L. Koenig, J. Chem. Phys., 1970, 53, 1126–1130 CrossRef CAS .
  47. H. Murphy, P. Papakonstantinou and T. I. T. Okpalugo, J. Vac. Sci. Technol., B: Microelectron. Nanometer Struct.–Process., Meas., Phenom., 2006, 24, 715–720 CrossRef CAS .
  48. V. Datsyuk, M. Kalyva, K. Papagelis, J. Parthenios, D. Tasis, A. Siokou, I. Kallitsis and C. Galiotis, Carbon, 2008, 46, 833–840 CrossRef CAS .
  49. S.-Y. Yang, C.-C. M. Ma, C.-C. Teng, Y.-W. Huang, S.-H. Liao, Y.-L. Huang, H.-W. Tien, T.-M. Lee and K.-C. Chiou, Carbon, 2010, 48, 592–603 CrossRef CAS .
  50. K. A. Wepasnick, B. A. Smith, K. E. Schrote, H. K. Wilson, S. R. Diegelmann and D. H. Fairbrother, Carbon, 2011, 49, 24–36 CrossRef CAS .
  51. C. H. Lau, R. Cervini, S. R. Clarke, M. G. Markovic, J. G. Matisons, S. C. Hawkins, C. P. Huynh and G. P. Simon, J. Nanopart. Res., 2008, 10, 77–88 CrossRef CAS .
  52. X. Lu, S. Francis, D. Motta, N. Dimitratos and A. Roldan, Phys. Chem. Chem. Phys., 2020, 22, 3883–3896 RSC .
  53. Q. Zhai, Z. Xia and L. Dai, Catal. Today, 2023, 418, 114129 CrossRef CAS .

Footnote

Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3dt02310a

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