Catalytic performance of bismuth pyromanganate nanocatalyst for Biginelli reactions

Abstract

Bi₂Mn₂O₇ nano-powders were synthesized via a stoichiometric 1 : 1 Bi : Mn molar ratio hydrothermal method at 180 °C for 48 h in a 1 M NaOH aqueous solution. Ultraviolet-visible (UV-vis) spectrum analysis showed that the nanostructured Bi₂Mn₂O₇ powders possessed strong light absorption in the ultraviolet region and the direct band gap energy was obtained from the UV-vis spectrum. The as-prepared nanomaterial exhibited high catalytic activity in the one-pot synthesis of the heterocyclic compounds 3,4-dihydropyrimidin-2(1H)-ones (DHPMs) in Biginelli reaction. Experimental design was used to find the optimized reaction conditions. Reusability of the catalyst was also investigated.

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1. Introduction

Pyrochlore materials with general formula A₂B₂X₇ in which A and B are cations and X is O²⁻ or F⁻ anions have found key applications in catalysis,^1–4 electronics,⁵ optical and magnetic devices,^6,7 and solid oxide fuel cell (SOFC) electrode materials.⁸ Bi-containing perovskites have received a lot of attention as lead-free ferroelectric^9,10 and multi-ferroic materials.^11,12 Bi₂Mn₂O₇ has already been prepared by a conventional solid state reaction¹³ and hydrothermal¹⁴ methods and has been reported to have catalytic ability for oxygen reduction.¹⁵

The Biginelli reaction is a methodology for the one-pot synthesis of 3,4-dihydropyrimidin-2-(1H)-one derivatives (DHPMs).^16,17 DHPMs have already shown biological activities.¹⁸ Several metal oxides have been reported as nanocatalysts for the Biginelli reactions including alumina supported Mo catalysts,¹⁹ nano ZnO as a structure base catalyst,²⁰ MoO₃–ZrO₂ nanocomposite,²¹ MnO₂–MWCNT nanocomposites,²² TiO₂ nanoparticles,²³ Mg–Al–CO₃ and Ca–Al–CO₃ hydrotalcite,²⁴ Bi₂O₃/ZrO₂ nanocomposite,²⁵ ZrO₂–Al₂O₃–Fe₃O₄,²⁶ imidazole functionalized Fe₃O₄@SiO₂,²⁷ alumina supported MoO₃,²⁸ ZrO₂-pillared clay,²⁹ ZnO nanoparticle,³⁰ Fe₃O₄–CNT,³¹ TiO₂–MWCNT,³² Fe₃O₄@mesoporous SBA-15,³³ Bi₂V₂O₇.³⁴ It should be noticed that in Biginelli reaction, not only do the type and amount of a catalyst influence the efficiency of the reaction, but also the amount of the analyte, temperature and reaction time are important factors which must be optimized. These factors have usually been optimized with one-factor-at-a time method (OFAT), which varies one factor at a time while holding all others fixed. However, statistically designed experiments^35,36 that vary several factors simultaneously are more efficient when studying two or more factors. It is so because it requires less resources (experiments, time, material, etc.) for the amount of information obtained. Moreover the estimates of the effects of each factor are more precise. To the best of our knowledge there is no report about application of experimental design to optimize this reaction. There were two main goals in this study. The first one was the study of the optical properties of Bi₂Mn₂O₇ nanomaterial and estimating its band gap energy using ultraviolet-visible spectrum. The second aim was investigating the catalytic efficiency of Bi₂Mn₂O₇ in Biginelli reaction whose experimental condition was optimized by experimental design. Full factorial design coupled with response surface methodology³⁷ was used in this purpose. In a previous work, we have reported the use of Bi₂V₂O₇ as catalyst for Biginelli reactions. Here we kept the softer metal ion, i.e. Bi³⁺ unchanged but the harder metal ion was changed to see its effect in the studied catalytic reaction. It was found that the synthesized Bi₂Mn₂O₇ nanocatalyst had excellent efficiency in the synthesis of DHPMs. Besides, the band gap energies were correlated to the catalytic performance of the catalyst. The reusability of the nanocatalyst was also investigated.

2. Experimental

2.1. General remarks

All chemicals were of analytical grade, obtained from commercial sources, and used without further purification. Bi₂Mn₂O₇ nanocatalyst was prepared following our previously reported method.¹⁴ Absorption spectrum was recorded on an Analytik Jena Specord 40 (Analytik Jena AG Analytical Instrumentation, Jena, Germany). The purity of products was checked by thin layer chromatography (TLC) on glass plates coated with silica gel 60 F254 using n-hexane/ethyl acetate mixture as mobile phase and comparison of melting points with authentic samples. Melting points were obtained on a thermoscientific 9100 apparatus.

2.2. General procedure for the synthesis of DHPMs

In a typical procedure, a mixture of aldehyde (1 mmol), ethyl acetoacetate (1 mmol), urea (1.2 mmol) and 0.014 g (2.2 × 10⁻² mmol) of Bi₂Mn₂O₇ (M_w = 639.88 g mol⁻¹) as catalyst were placed in a round-bottom flask under solvent free conditions. The suspension was stirred at 104 °C. The reaction was monitored by thin layer chromatography (TLC) [6 : 4 hexane : ethylacetate]. After completion of the reaction, the solid crude product was washed with deionized water to separate the unreacted raw materials. The remaining solid was then dissolved in ethanol to separate the heterogeneous catalyst. The solid catalyst was washed with acetone and dried in oven at 90 °C to be used in the next cycles. The ethanolic solution was evaporated to dryness to obtain the target DHPMs.

2.3. Experimental design and achieving optimal conditions in Biginelli reactions

There is a large amount of experimental designs in the literature to explore the optimal level of the factors affecting a chemical reaction. One of the common designs is full factorial design^38,39 which is defined by all possible combinations of the factors and their settings. Suppose that there are k investigating factors and that each factor can be set to m different levels. The number of possible combinations of the factors and their settings will then be m^k. In chemical systems, two levels of the factor setting is prevalent because such designs permit the determination of all main effects and all interaction effects with small number of experiment.

The relation between factors and response is theoretically modeled by a function that is the underlying physical mechanism to the problem under investigation. This relation causes the reproducibility in the phenomenon under study to be able to experiment with it and to interpret the results. Response surface methodology (RSM) is a mathematical and statistical method, which analyzes experimental design by applying an empirical model.³⁷ The adequacy of the applied model is checked using analysis of variance (ANOVA)³⁸ which needs some replicate experiments.

In our study, in Biginelli reaction, the goal was to determine how much nanocatalyst should be used, and at which temperature and time the reactants should be monitored. The response was the obtained yield (%). Different possible combinations of these factors were designed which reported in Table 1. Here, four replicates at the center of factors were considered for the validation of the model by ANOVA (Table 1). All the experiments were done at two days with random order.

Table 1 Two-level full factorial design in Biginelli reaction^a

	Catalyst (g)	Temp (°C)	Time (min)	Yield (%)
a Benzaldehyde : ethylacetoacetate : urea molar ratios is as follows: 1 : 1 : 1.2.
Day 1	0.041	56	66	31
Day 1	0.014	56	24	15
Day 1	0.028	80	45	54
Day 1	0.014	104	66	96
Day 1	0.041	104	24	85
Day 1	0.028	80	45	50
Day 2	0.041	56	24	23
Day 2	0.014	56	66	15
Day 2	0.028	80	45	54
Day 2	0.028	80	45	50
Day 2	0.014	104	24	85
Day 2	0.041	104	66	96
Day 2	0.005	104	66	84

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The observed data of the factorial design was fitted to a linear response model. Prior to the analysis, low and high factor levels were coded to −1 and +1, respectively. Eqn (1) shows the relation between the factors and the yield of the reaction, Y%, based on the first order model:

Y% = 54.49 + 2.88 × catalyst + 34.25 × Temp. + 3.78 × time (1)

The coefficient of the equation shows the effect of the parameters. The more the value is, the more the effect is. It is clear that the effect of temperature is higher than the effect of the others, moreover, the effect of the catalyst and time are close to each other.

The ANOVA results listed in Table 2 shows that the p-value of the regression was smaller than 0.05, indicating that the model was significant at a high confidence level (95%).³⁷ The p-value probability of lack of fit was greater than 0.05, which confirmed the models' significance. Also the coefficient of determination (the R-square, adjusted-R-square) was used to express the quality of fit of polynomial model equation. In this case, R² of variation fitting for Y% 0.9840 indicated a high degree of correlation between the response and the independent factors. Also, the high value of adjusted regression coefficient (R²-adj = 0.9771) indicated high significance of the proposed model.

Table 2 Analysis of variance for suggested first-order model^a

Source	DF	Seq SS	F	P
a For detailed explanation of the table, refer to ref. 38.
Block	1	4.94
Regression	3	9739.51	143.53	<0.0001
Residual error	7	158.33
Lack-of-fit	5	142.33	3.56	0.2338
Pure error	2	16.00
Total	11	14207.3

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To illustrate the effects in the above models, the three-dimensional (3D) response surfaces plot of the response (using eqn (1) when the amount of time was fixed at optimal level and the other two were allowed to vary) is shown in Fig. 1.

Fig. 1 Response surface plots of Y% vs. catalyst and temperature at fixed level of time (66 min) parameter.

3. Result and discussion

3.1. Optical property

UV-vis absorption spectrum of the Bi₂Mn₂O₇ nanomaterial is shown in Fig. 2a. The direct optical band gap is also shown in Fig. 2b. The pure Bi₂Mn₂O₇ nanomaterial displayed typical visible absorption edges at about 324 and 420 nm. According to the results of Pascual et al.,³⁹ the relation between the absorption coefficient and incident photon energy can be written as (αhν)² = A(hν − E_g), where A and E_g are constant and direct band gap energy, respectively. Band gap energy was evaluated by extrapolating the linear part of the curve to the energy axis. It was found that the band gaps were 2.75 and 3.78 eV.

Fig. 2 Plots of (a) UV-vis spectrum and (b) (αhν)² versus hν for Bi₂Mn₂O₇.

3.2. Catalytic studies

The goal of the optimization was to maximize the yield of the reaction corresponded to the condition of experiment in which the above equation was maximized. The results showed that 0.014 g of the catalyst, 104 °C reaction temperatures, and 66 min reaction time were the optimum parameters for the synthesis of DHPMs, which was one of the experimental points in the design (Table 1). In short, the intelligent experimental design in this work led to find the optimal condition of the reaction with small number of experiments rather than one-at-a-time method. By means of RSM method, it confirmed that the factors were independent and their effects on the reaction was depicted and interpreted. The optimized parameters were used for the synthesis of other derivatives and the results were collected in Table 3. Scheme 1 shows a summary of the reaction pathway.

Table 3 Biginelli reactions using ethyl/methyl acetoacetate and urea with different benzaldehyde derivatives

R1	R2	Yield%	Mp (°C)
			Found	Reported
H	OEt	96	199–201	198–200 (ref. 25)
4-Cl	OEt	89	207–209	209–211 (ref. 25)
2-Cl	OEt	86	216–218	215–217 (ref. 25)
4-Br	OEt	80	210–212	213–214 (ref. 27)
3-NO2	OEt	96	224–227	225–226 (ref. 30)
2-OMe	OEt	65	260–263	262–263 (ref. 27)
3-OMe	OEt	36	255–258	257–259 (ref. 22)
3-OH	OEt	53	165–168	164–165 (ref. 30)
4-OH	OEt	46	250–252	255–257 (ref. 22)
H	OMe	84	201–203	206–207 (ref. 40)
4-Cl	OMe	86	202–204	204–207 (ref. 41)
2-Cl	OMe	92	225–228	228–229 (ref. 40)
4-Br	OMe	68	236–239	242–244 (ref. 40)
3-NO2	OMe	98	278–281	279–280 (ref. 41)
2-OMe	OMe	51	280–283	283–285 (ref. 42)
3-OMe	OMe	29	193–195	192–195 (ref. 43)
4-OH	OMe	34	239–241	241–242 (ref. 44)

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Scheme 1 Schematic representation of the reaction pathway for the synthesis of DHPMs.

Table 4 shows the catalytic efficiency of the synthesized Bi₂Mn₂O₇ nanomaterial compared to the starting materials Bi(NO₃)₃ and MnO₂. The optimized conditions from the previous section were used. As could be seen from Table 4, Bi₂Mn₂O₇ was much more efficient catalyst compared to the two starting materials which mean that the presence of the both metal ions was important and the two ions have acted cooperatively. Cooperative catalysis has been reported to accelerate some especial chemical transformations and has gained much attention recently.^45,46 In this approach, the presence of at least two different catalysts, here the two metal ions with different hardness/softness, is necessary to activate substrates simultaneously. Bi³⁺ is the softer metal ion while Mn⁴⁺ is the harder one. In the Biginelli reactions, the substrates have different hardness and the presence of two different metal ions with different carbophilicity will definitely better interact with a broader range of such substrates.

Table 4 Comparison study of the catalytic ability of the synthesized Bi₂Mn₂O₇ with raw materials

Catalyst	Reagents	Time (min)	Yield (%)
Bi2Mn2O7	Benzaldehyde	66	96
MnO2	Benzaldehyde	66	47
Bi(NO3)3	Benzaldehyde	66	42

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In an earlier work, we have reported the catalytic efficiency of Bi₂V₂O₇ in the same Biginelli reactions. In this work we kept the soft metal ion, i.e. Bi³⁺, unchanged but changed the harder metal ion. It was found that by changing this metal ion, the band gap energy was considerably changed. Table 5 shows the band gap data for Bi₂Mn₂O₇, Bi₂V₂O₇ and some of the previously reported catalysts as well as a comparison of their catalytic performance in two different types of organic syntheses reactions, i.e. the synthesis of DHPMs (entry 1 and 2) and 5-substituted 1H-tetrazoles (entry 3–6). According to the DFT calculations performed by Andrew Bean Getsoian et al.⁴⁷ band gap plays an important role in the catalytic activity of a catalyst. They have theoretically shown that the more the band gap energy, the more the activation energy and the less the catalytic activity. Our experimental results confirm this finding (Table 5). To show the merit of the present work, we have compared Bi₂Mn₂O₇ nanocatalyst results with some of the previously reported catalysts in the synthesis of DHPMs (Table 6). It is clear that Bi₂Mn₂O₇ showed greater activity than some other heterogeneous catalysts.

Table 5 Catalytic activities data for different catalysts

Entry	Sample	Band gap (eV)	Derivative	Catalyst amount	Condition	Yield (%)	Ref.
1	Bi2Mn2O7	2.75 and 3.78	4-Cl	2.2 mmol%	Solvent-free, 104 °C	89	This work
			2-Cl			86
			2-OMe			65
2	Bi2V2O7	2.0	4-Cl	3.1 mmol%	Solvent-free, 90 °C	92	34
			2-Cl			98
			2-OMe			98
3	[Cu(II)-PhTPY]	3.54 and 4.3			100 °C, 5 h, DMF (solvent)	90	48
4	ZnO	3.34		0.1 g	125 °C, 14 h, DMF	92	49
5	Ag	3.0		30 mol%	120 °C, 8 h, DMF (solvent)	94	50
6	Au	2.35		10 mol%	80 °C, 1.3 h, DMF (solvent)	96	51

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Table 6 Comparison study of the catalytic ability of the synthesized Bi₂Mn₂O₇ with other catalysts

Catalyst	R1	Catalyst amount	Reaction condition	Yield%	Time (min)	Ref.
Bi2Mn2O7	H	2.2 × 10^−2 mmol	Solvent-free, 104 °C	96	66	This work
	4-Cl			89
	2-Cl			86
Mo/γ-Al2O3	H	0.3 g	Solvent-free conditions at 100 °C	80	60	28
Bi2O3/ZrO2	H	20 mol%	Solvent-free, 80–85 °C	85	120	31
	4-Cl			85	120
	2-Cl			82	165
ZrO2–Al2O3–Fe3O4	H	0.05 g	Ethanol, reflux, 140 °C	82	300	32
	4-Cl			66
	2-Cl			40
Bi2V2O7	H	3.1 × 10^−2 mmol	Solvent-free, 90 °C	89	60	34
	4-Cl			92
	2-Cl			98
ZnO	H	25 mol%	Solvent-free conditions at 90 °C	92	50	38
	4-Cl			95

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3.3. Reusability of the catalyst

For practical applications of this heterogeneous catalyst the level of reusability was also tested. The recycled catalyst could be reused for at least four times with small decrease in yield (Fig. 3).

Fig. 3 Reusability of Bi₂Mn₂O₇ in Biginelli reaction.

4. Conclusion

In this work, the catalytic application of the synthesized nanomaterial Bi₂Mn₂O₇ was investigated in Biginelli reaction in solvent free conditions. Experimental design was used to find the optimized conditions. It was found that Bi₂Mn₂O₇ nanomaterial had excellent efficiency in the synthesis of DHPMs. The reusability investigations showed that the synthesized nanocatalyst had good stability and performance for the synthesis of DHPMs.

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