Trace papaverine analysis in biological samples after preconcentration by UA-D-μ-SPE method using a new magnetic GO–MOF nanocomposite

Abstract

The present contribution describes the synthesis of a novel, green, and cost-effective UA-D-μ-SPE (ultrasound-assisted dispersive micro-solid phase extraction) method employing a magnetic graphene oxide/metal–organic framework (GO/MOF) nanocomposite as a sorbent for specific preconcentration of papaverine from complex biological media. The GO/MOF nanocomposite synthesized by design possesses highly well-defined structure by FT-IR, FE-SEM, XRD, EDS, VSM, and BET studies, which proved to exhibit an ample specific surface area, enhanced sorption capacity, high chemical and thermal stability, rapid adsorption rate, and high reusability. The magnetic nature ensures easy separation without centrifugation, and thus the process of extraction is efficient and easy to run. Optimization of the key extraction parameters was regularly carried out with central composite design (CCD) in order to achieve an efficient operation with the least consumption of resources in keeping with green analytical chemistry. The method is of very good analytical performance with a LOD and LOQ of 0.09 ng mL⁻¹ and 0.3 ng mL⁻¹, respectively, with intra- and inter-day precision of 3.3% and 4.7% at 10 ng mL⁻¹. Application to biological samples resulted in good recoveries (95.24–98.08%), even from intricate matrices. The novel UA-D-μ-SPE technique, coupled with HPLC–UV detection, not only yields enhanced sensitivity, selectivity, and environmental sustainability but also reduces sample preparation time and operational cost significantly. Incorporation of state-of-the-art nanocomposite sorbents and comprehensive optimization emphasizes the method’s potential as a reliable tool for trace-level pharmaceutical analysis in biomedical and clinical research.

Introduction

Papaverine, an alkaloid derived from opium, is a drug which has the capacity to induce relaxation of smooth muscle and hence influence both arterial and venous structures but its use has been reduced or discontinued in many medical centers due to serious side effects¹. These issues highlight the need for the development of an effective sample preparation protocol for accurate quantification of papaverine in biological matrices². In practice, analyte concentrations are typically below detection limits of typical analytical techniques, and the complexity of biological matrices may be an impediment to exact results based on matrix interferences on both the analyte and the detection system³. Therefore, the development of an effective and selective sample preparation method is essential to ensure reliable quantification. Conventional analytical methods often lack the sensitivity and preconcentration capability required for such analyses, highlighting the need for advanced extraction and enrichment techniques⁴.

In recent years, sorbent-based extraction methods such as SPE and its modified forms (DSPE, MSPE, and MDSPE) have been widely applied to overcome limitations of conventional cartridge-based SPE, including clogging and time-consuming procedures^5,6. Among these, dispersive micro-solid-phase extraction (D-μ-SPE) offers notable advantages for pretreatment of complex biological samples due to its operational simplicity, low solvent consumption, and good compatibility with green analytical chemistry principles^7–9. The application of ultrasound-assisted D-μ-SPE has further improved extraction efficiency by accelerating mass transfer and enhancing dispersion of sorbent particles. However, the success of the advanced extraction techniques largely relies on the selection of appropriate sorbents. Therefore, developing high-capacity, stable, and reusable nanosorbents remains a key challenge in the efficient extraction of drugs from complex biological matrices^10–12.

Graphene oxide (GO), a two-dimensional carbon-based material rich in oxygenated functional groups (hydroxyl, epoxy, and carboxyl), has attracted significant attention as a versatile sorbent for analytical sample preparation due to its high surface area, tunable chemistry, and ease of functionalization^13,14. These active functionalities facilitate effective interactions with target analytes via hydrogen bonding, π–π interactions, van der Waals forces, and electrostatic attractions and thus enhance extraction efficiency and selectivity even in complex biological matrices^15,16. However, pristine GO suffers from limited structural stability and aggregation in aqueous environments, which restrict its performance under biological conditions. To address these drawbacks, researchers have increasingly focused on hybridization of GO with other nanostructured materials—particularly metal–organic frameworks (MOFs)—to exploit their complementary features.

MOFs, a class of crystalline porous materials constructed from metal ions and organic linkers, provide exceptionally very high internal surface area, tunable porosity, and excellent structural flexibility¹⁷. Compared with traditional porous materials such as zeolites, MOFs offer precise tuning of the cavity size, surface area, and functionalization through the judicious selection of building blocks and synthesis conditions, placing them at the vanguard of advanced sorbent materials research^18,19. Recent studies have highlighted the unique potential of GO–MOF nanocomposites in improving drug extraction from biological matrices by enhancing extraction efficiency, selectivity, chemical stability, and reusability in sample preparation processes^20–22. In such hybrid systems, GO facilitates uniform MOF crystal growth and prevents aggregation, while the MOF framework introduces abundant and selective binding sites for target analytes. The synergistic interfacial coupling between GO and MOF results in improved mass transfer, structural robustness, and reusability—addressing current challenges in developing efficient, stable, and sustainable nanosorbents for trace-level drug analysis in complex biological samples. Nonetheless, effective separation of the adsorbent from the sample matrix is still an enormous challenge. To address this issue, incorporation of magnetic nanoparticles into sorbent systems has proven highly beneficial, allowing rapid and clean separation of the adsorbent under an external magnetic field without additional filtration or centrifugation steps²³. This strategy not only simplifies the extraction workflow but also minimizes analyte loss and matrix interference, which are critical for accurate drug quantification. Furthermore, optimization of extraction parameters has been greatly enhanced by the adoption of chemometric modeling. This approach, in addition to minimizing the number of experimental runs, explains complex interactions among variables, as well as optimizing extraction efficiency and analytical method reliability. Response surface methodology, for instance, is an effective tool used for optimum condition search, predictive mathematical modeling, and analyzing the influence and interaction of various parameters on analytical responses²⁴. Collectively, such material design innovation and methodological advancements have paved the way to more cost-effective, sustainable, and high-performance sample preparation techniques for real biological sample analysis¹⁵.

The primary objective of the current research was to devise an extremely efficient UA-D-µ-SPE method to selectively recover papaverine from complex biological matrices with subsequent HPLC–UV quantification. The essence of the innovativeness of this study is based on the development and employment of a new magnetic GO-MOF nanocomposite material as a highly advanced sorbent material during the extraction. The integration of GO and MOF components provides a synergistic enhancement in adsorption capacity, selectivity, and structural stability compared with their individual counterparts, thereby overcoming common limitations of conventional sorbents in drug extraction. Besides, an interesting and innovative aspect of this research is the systematic optimization of extraction parameters through Central Composite Design (CCD), a mathematical and statistical approach rarely applied in comparative studies. This chemometric optimization not only minimizes the numbers of experimental trials—thereby increasing environmental sustainability, cost-effectiveness, and time efficiency but also enables variable interaction assessment at a full scale and offers more robust and better-performing analyses. In general, such methodological advancements make this work stand out from existing literature by offering an environmentally friendly and high-performance approach for trace-level pharmaceutical analysis in biomedical research.

Experimental

Chemicals and solvents

All of the solvents and reagents applied in this study were purchased from reliable commercial suppliers to ensure analytical purity. Papaverine, which was used as the reference analyte, was generously donated by Razi Pharmaceutical Company (Tehran, Iran). Stock solution of papaverine at 1 mg mL⁻¹ was stored in polypropylene vials at 4 °C, protected from heat and light to prevent degradation. Standard working solutions were made daily with freshly prepared solutions by precise dilution of the stock solution with deionized water for precision and reproducibility.

Chromatography-grade solvents, including methanol, water, and acetonitrile, were also bought from Amertat Shimi Company (Tehran, Iran) to perform the chromatographic operation at their best. Hydrochloric acid (HCl), phosphoric acid (H₃PO₄), sodium hydroxide (NaOH), ferric chloride (FeCl₃), ferrous sulfate (FeSO₄), ammonia (NH₃), dimethylformamide (DMF), chloroform (CHCl₃), terephthalic acid (C₈H₆O₄), zinc nitrate hexahydrate (Zn(NO₃)₂·6H₂O), and trichloroacetic acid (CCl₃COOH) used in the experiments were analytical grade and were bought from Merck (Munich, Germany).

99% pure graphene oxide (GO) was bought from US Research Nanomaterials (Houston, Texas, USA) to synthesize nanocomposite adsorbent. Sodium chloride (NaCl, 99.5% analytical grade) as an ionic strength controller was sourced from Scharlau (Barcelona, Spain). All the laboratory glassware was carefully pre-cleaned with acetone and hexane and then dried at 100 °C for 2 h to ensure the complete removal of any remaining organic impurities. Ultra-pure water was produced on a Milli-Q purification system (Millipore, Bedford, MA, USA) and applied in all experiments.

Instruments

Chromatography was accomplished on a Knauer high-performance liquid chromatography (HPLC) instrument (Berlin, Germany), which included an HPLC pump K-1001 for precise mobile-phase supply, an ultraviolet detector K-2600, a degasser D-14163, and a Rheodyne 7725i injector (IDEX Corporation, Rohnert Park, CA, USA) with a 20 μL sample loop. The target analyte was separated on a C18 column (150 mm × 4.6 mm i.d., 5 μm particle size), and the detection wavelength was carefully adjusted at 249 nm. The mobile phase constituted a ternary mixture of acetonitrile, methanol, and 0.05 mol L⁻¹ phosphate buffer (pH 3) in the volumetric proportion of 30:50:20 (v/v/v), which was delivered at a constant flow rate of 1.0 mL min⁻¹ under isocratic conditions.

Adjustments of solution pH were accomplished using a Hanna pH meter (model HI 2211, Woonsocket, RI, USA) for maintaining optimal extraction conditions. Phase separations were conducted by a Hettich centrifuge (model EBA 20, Tuttlingen, Germany), and all the solid samples were precisely weighed by a Shimadzu analytical balance (model AEU-210, Kyoto, Japan). The nanocomposite adsorbent was prepared in an Atera furnace (model AFE1200L, Qazvin, Iran). An ultrasonic bath (model SW3-type, SONO SWISS, Switzerland) with 80 W power and 50/60 kHz frequency was employed for nanocomposite dispersion and elution solvent in sample matrix and efficient and homogeneous mixing.

Exhaustive characterisation of the developed nanocomposite was performed by combining advanced analytical tools. Identification of functional moieties was done by Fourier-transform infrared spectroscopy (FT-IR, model 8400, Shimadzu, Japan). Specific surface area was determined using Brunauer–Emmett–Teller (BET) analysis (BELSORP-miniX, Paris, France). Surface topography was scrutinized with field emission scanning electron microscopy (FE-SEM, TESCAN BRNO-Mira3 LMU, Kohoutovice, Czech Republic), while crystalline structure was determined by X-ray diffraction (XRD, Bruker D8 Advance, Billerica, MA, USA). Elemental composition was analyzed by energy-dispersive X-ray spectroscopy (EDS, Mira3 Detector SAMx, Kohoutovice, Czech Republic) and magnetic character was analyzed by vibrating sample magnetometer (VSM, VSM1100, Weistron, Taiwan).

Fabrication of magnetic GO-MOF nanocomposite

GO preparation

For effective removal of the GO surface impurities, 0.1 g GO was incorporated into 100 mL methanol–hydrochloric acid solution (6 mol L⁻¹ HCl, 1:1 v/v). Ultrasonic treatment using a water bath for 40 min was utilized for complete dispersion of the GO nanoparticles. The suspension was sonicated and then magnetically stirred for 24 h for thorough purification. The suspension obtained was then filtered through qualitative filter paper and the GO retained was washed several times with distilled water until pH became neutral. The purified GO was then dried under 70 °C in an oven to yield a material for future nanocomposite synthesis.

Synthesis of GO-MOF nanocomposite

To functionalize graphene oxide (GO) with a metal–organic framework (MOF) on the surface, 0.6 g of pretreated GO was uniformly dispersed in 120 mL of dimethylformamide (DMF) by strong agitation. Next, 4.92 g of Zn(NO₃)₂·6H₂O and 1.056 g of terephthalic acid (C₈H₆O₄) were introduced into the suspension. The resulting mixture was subjected to ultrasonic irradiation for 15 min for homogeneous dispersion and the facilitation of the initial coordination process. The mixture was subsequently loaded into Teflon-lined stainless steel autoclave and subjected to 120 °C for 10 h to promote the in situ growth of MOF on GO. Once the reaction was complete, product was filtered and washed multiple times three times with DMF and CHCl₃ to remove any unreacted precursors and excess solvent. The GO-MOF nanocomposite was then dried in oven at 70 °C for 24 h to yield a hybrid material with enhanced structure and function properties to undergo further magnetization and analytical uses.

Synthesis of the magnetic GO–MOF nanocomposite

In order to impart magnetic properties to the as-synthesized GO–MOF nanocomposite, ferric chloride (FeCl₃) (1.5 g) and ferrous sulfate (FeSO₄) (0.75 g) were dissolved in 150 mL of pure water underneath agitation (stirring) conditions. The aforementioned GO–MOF nanocomposite was then added to it and the resulting suspension was processed under ultrasonic conditions at 60 °C for 30 min for uniform dispersion and facilitating nucleation of the magnetic nanoparticles. Subsequently, the ammonia solution was dropwise added to mixture up to pH 10–11, thereby allowing the co-precipitation of iron oxides on the nanocomposite surface. Upon pH adjustment, the resultant mixture was then subjected to a further 10 min of ultrasonic irradiation at 60 °C in an effort to complete the magnetization process. The formed magnetic nanocomposite was thereafter efficiently separated from the reaction medium using an external magnet, rinsed well a few times with distilled water to achieve neutral pH, and lastly oven-dried at 70 °C. This gave a magnetic GO–MOF nanocomposite with enhanced separation capability and use for advanced extraction procedures.

Extraction procedure

After primary evaluations and optimizations, the sample preparation protocol was elaborately designed as follows: A 10.0 mL aliquot of sample solution, having the desired concentration of papaverine and adjusted to pH 8.0, was transferred into a 15.0 mL glass test tube. Afterwards, 8.5 mg of the synthesized magnetic GO–MOF nanocomposite was added to solution. The mixture was subjected to ultrasonic agitation for 90 s to facilitate rapid and homogeneous dispersion of the nanocomposite and enable efficient adsorption of the analyte. Following this, the nanocomposite was promptly isolated from solution, exploiting an external magnet. The adsorbed papaverine was desorbed by exposing nanocomposite to 220 μL methanol under ultrasonic agitation for 65 s. Finally, a 20 μL aliquot of the final eluent was injected into HPLC system for quantitation.

Initial sample preparation

All applicable rules and regulations were obeyed with related methods, obtaining and using the following real samples. The Semnan University Ethics Committee in Semnan, Iran, gave its approval to the experimental procedures (Approval No. Z875/05/2023). Every participant in the research gave their informed permission.

Urine sample The urine sample was filtered using a 0.42 μm pore-size filter paper after collection from a healthy donor in order to remove particulate material prior to storage in a polypropylene tube at refrigerated conditions to preserve the integrity of the sample. A 5 mL aliquot was diluted to 50 mL with distilled water prior to analysis in order to achieve the appropriate matrix conditions for extraction¹².

Plasma sample A plasma sample was taken from a healthy donor through the Semnan Blood Transfusion Organization. The deproteinization and matrix adjustment were accomplished by adding 0.2 g of trichloroacetic acid (CCl₃COOH) and 0.5 mL of concentrated HCl (hydrochloric acid) to 5 mL of plasma. The blend was ultrasonically homogenized for 20 min to precipitate every protein. After centrifugation, 1 mL of supernatant was diluted to 50 mL using pure water in order to create a sample that was acceptable for further extraction²⁵.

Breast milk sample An addition of 3 mL of acetonitrile and 0.2 g trichloroacetic acid to 10 mL of breast milk was made to enable precipitation of proteins and extraction of lipids. The solution was allowed to stay in the ultrasonic bath for 20 min to facilitate proper homogenization. Then, 1 mL of treated sample was diluted with distilled water to 50 mL to obtain the final working solution²⁶.

Saliva sample Saliva was obtained from a normal volunteer and first centrifuged at 3000 rpm for 5 min to remove cellular debris and suspended solids. Then, 1 mL of the clarified saliva was diluted up to 10 mL with distilled water, readying the sample for analytical extraction²⁷.

Computation of ER (extraction recovery) and EF (enrichment factor)

In order to optimize the experimental conditions, two key parameters—extraction recovery (ER, %) and enrichment factor (EF)—were examined systematically in order to determine the extraction performance. The enrichment factor (EF) was determined using the following equation:

1

where C_f represents final concentration of analyte in eluent solution, and C₀ is original concentration of analyte in sample solution.

Extraction recovery (ER, %) is ratio of amount of whole sample of analyte in original sample (n_i) that is properly transferred into eluent phase (n_f), and is calculated from following relation:

2

where n_i and n_f refer to the number of moles of the analytes in sample solution and eluent solution and V_s and V_e denote volumes of aqueous sample and eluent solution, respectively.

Results and discussion

Characterization of the synthesized nanocomposite

In order to fully characterize the magnetic GO–MOF nanocomposite, an arsenal of advanced analytical tools was employed to elucidate its morphological, structural, and physicochemical characteristics. FE-SEM was employed for examining the surface morphology and the particle size distribution of the nanocomposite. XRD analysis was employed for examining the crystalline nature, whereas elemental distribution and composition were determined by EDX. The magnetic properties were measured by VSM (vibrating sample magnetometry). The specific surface area and porosity were analyzed by Brunauer–Emmett–Teller (BET) analysis. The nature and functionality of groups in nanocomposite structure were identified by FT-IR (Fourier-transform infrared spectroscopy). Collectively, these thorough analyses provided detailed information regarding the structural stability, surface characteristics, magnetic responsiveness, and chemical properties of the synthesized magnetic GO–MOF nanocomposite, confirming its suitability for application in novel extraction techniques.

FE-SEM pattern

The morphological characteristics of the nanocomposite synthesized were accounted for using FE-SEM. The resulting micrographs (Fig. 1a–c) well illustrate that both MOF structures and Fe₃O₄ nanoparticles are decorated onto the graphene oxide sheets. Such well-established distribution of MOF and magnetic nanoparticles on the GO substrate confirms the successful synthesis of the desired nanosorbent and the effective integration of its building blocks. Although Fe₃O₄ nanoparticles are visible on the surface of the nanocomposite in the SEM images, they are partially embedded within the MOF framework and GO layers, which effectively protect them from oxidation by forming a passivating coating composed of oxygen-containing groups and organic ligands.

Fig. 1.

FESEM micrograph (GO, GO-MOF and Magnetic GO-MOF) (a–c), FT-IR spectrum (d), XRD pattern of magnetic nanocomposite (e), BET isotherm of magnetic nanocomposite (f), EDX spectrum of magnetic nanocomposite (g–h) and VSM plot of magnetic nanocomposite (i).

FT-IR pattern

To describe presence of functional moieties and to validate successful synthesis of the magnetic GO–MOF nanocomposite, FT-IR spectroscopy was used. As shown in Fig. 1d, the FT-IR spectrum of GO exhibits characteristic absorption bands corresponding to oxygen-containing groups, including bending and stretching C=O, stretching C–O, and a broad O–H stretching band. After formation of the magnetic GO–MOF nanocomposite, several new absorption peaks appeared and some bands slightly shifted, confirming chemical interactions among GO, Fe₃O₄ nanoparticles, and MOF crystals. FT-IR spectrum of synthesized nanosorbent exhibits well-retained absorption bands at 578, 740, 908, 1184, 1244, 1280, 1467, 1652, and 3440 cm⁻¹. These characteristic peaks correspond to, in sequence, Fe–O bending vibrations (578 cm⁻¹), Zn–O bending vibrations (740 and 908 cm⁻¹), bending C = O (1184 cm⁻¹), stretching C–O (1244 and 1280 cm⁻¹), CH₂ bending (1467 cm⁻¹), C = O stretching (1652 cm⁻¹), and hydrogen bonding of O–H (3440 cm⁻¹). The presence of these functional groups not only proves successful immobilization of the metal–organic framework and magnetic nanoparticles on the graphene oxide surface but also proves structural stability and multifunctionality of the as-prepared nanocomposite.

XRD pattern

X-ray diffraction (XRD) is a simple and non-destructive analytical technique that is widely exploited in characterization of crystalline materials. XRD provides valuable information on average particle size, crystallinity, lattice constants, and elemental composition. In this work, XRD analysis was conducted to confirm successful fabrication and structural stability of the magnetic GO–MOF nano-composite. The diffraction pattern exhibited intense peaks for MOFs and GO sheets at 2θ values of 18.30°, 08.43°, 47.53°, 96.56°, and 102.74°, indicating the occurrence of MOF phases in the composite. Additionally, peaks at 2θ values of 25.70°, 41.35°, 46.92°, and 64.62° were assigned to Fe₃O₄ nanoparticles (reference code 0629-19), which verifies the magnetic nature of the synthesized nanocomposite (Fig. 1e).

BET spectrum

As one could observe in Fig. 1f, as-synthesized magnetic GO–MOF nanocomposite had surface area 180.52 m² g⁻¹, pore volume 0.0310 cm³ g⁻¹, and mean pore diameter 6.69 nm. These values are in reasonable agreement with morphological features identified in corresponding FE-SEM micrographs. It is noteworthy that, although both metal–organic frameworks and graphene oxide sheets are individually well-known to possess very high surface areas, the surface area seen from the resulting nanocomposite is quite low. This reduction is because of several reasons including partial shielding of GO sheets by MOF particles, GO layer aggregation or stacking upon composite synthesis, and possible pore blocking or collapsing of structure occurring during synthesis and followed magnetization process. These mechanisms bring about a loss of accessible surface area and pore volume, but the composite retains the synergistic functions and enhanced adsorption capacities that both components have endowed. The porosity and surface area obtained through measurement are, nevertheless, sufficient to facilitate effective adsorption and extraction of the analyte, as seen in analytical performance of the nanocomposite in subsequent trials.

EDX spectrum

For elucidation of elemental composition of the synthesized nanocomposite, EDX analysis was performed. As observed in Fig. 1g, EDX spectrum of magnetic GO–MOF nanocomposite distinctly indicates signals of zinc (Zn), iron (Fe), carbon (C) and oxygen (O). The simultaneous presence of these characteristic peaks categorically confirms the successful deposition of both magnetic and metal–organic framework elements onto the graphene oxide matrix, hence bearing evidence to the efficient synthesis of the target nanocomposite. Also, the colored image obtained from EDX (Fig. 1h) illustrates the distribution of elements in the magnetic GO–MOF nanocomposite. This image clearly reveals their spatial distribution across the surface and structure of the nanocomposite.

VSM pattern

To identify magnetic properties of synthesized nanocomposite, VSM (vibrating sample magnetometry) was carried out. From Fig. 1i, the nanocomposite indicated a saturation magnetization value of 40 emu g⁻¹, thereby affirming its high magnetic sensitivity. The high magnetic moment facilitates easy and rapid separation of the nanocomposite from solution matrices upon exposure to an external magnetic field, further buttressing its use in sophisticated extraction schemes.

Based on the results of FT-IR, FE-SEM, XRD, EDS, BET, and VSM analyses, the synthesized GO/Fe₃O₄/MOF nanocomposite can be described as a hybrid material in which GO sheets are decorated with Fe₃O₄ nanoparticles and MOF crystals. The GO component serves as a robust and stabilizing support that provides graphenic π-surfaces, while the MOF contributes to an increased specific surface area and introduces abundant active adsorption sites. The Fe₃O₄ nanoparticles are uniformly distributed at the nanoscale across the GO–MOF matrix, avoiding aggregation and thus preserving the porosity and accessible surface area of the adsorbent. This well-organized structure facilitates strong interfacial contact and synergistic physicochemical interactions among the components, leading to enhanced adsorption performance.

The adsorption of papaverine molecules onto the GO/Fe₃O₄/MOF nanocomposite can be attributed to multiple interaction mechanisms, including: (i) π–π stacking between the aromatic rings of papaverine and the conjugated graphenic layers of GO; (ii) hydrogen bonding and electrostatic interactions between oxygen-containing functional groups of GO/MOF (and surface hydroxyl groups of Fe₃O₄) and the functional moieties of papaverine; and (iii) van der Waals forces within the porous MOF framework. Although excessive magnetic loading may hinder analyte diffusion in some systems, the optimized GO:MOF:Fe₃O₄ ratio in our composite maintained efficient mass transfer, as confirmed by the short equilibrium time observed in kinetic studies. From a thermodynamic viewpoint, the GO and MOF segments primarily contribute to the endothermic and spontaneous nature of the adsorption, while the Fe₃O₄ phase mainly provides structural and magnetic advantages that enable facile separation without compromising adsorption efficiency.

Procedure optimization

Response surface methodology (RSM) is a robust statistical design that uses mathematical modeling to interpret experimental data acquired from a systematic experimental matrix²⁸. Through empirical description of the relationship among experimental factors and analytical responses, RSM provides reliable estimation of optimal conditions for subsequent experimentation. Of many experimental matrices, Central Composite Design (CCD) has been a better approach for systematic optimization of analytical processes, particularly for multi-variable analysis. When combined with RSM, CCD is an extremely useful technique in modeling the complexity of data behavior, describing the effect and interaction of experimental factors, and ultimately optimizing analytical processes. This unified approach allows for straightforward identification of the optimum operating conditions and thus method efficiency and reliability²⁹.

To ensure maximum accuracy and effectiveness of method devised, optimization tests were accomplished meticulously in two separate CCD-based experimental designs, each targeted towards variables governing sorption and de-sorption stages, respectively. The primary variables influencing step of adsorption were pH of sample (A), quantity of nanosorbent (B), and ultrasonic agitation time (C), whereas the most critical parameters for desorption step were elution solvent volume (A) and ultrasonic time (B). The specific limits and scales for each variable are defined in Table 1. The overall optimization strategy not only ensures robustness and replicability of the extraction protocol but also demonstrates concern for conservation of resources and strict methodology.

Table 1.

The levels of variables in CCD for sorption and de-sorption stages.

Adsorption step
Independent variables	Spans and levels
	− α	− 1	0	+ 1	+ α
pH (A)	5.6	6.5	7.8	9.0	9.9
Nano adsorbent dosage (mg) (B)	4.6	6.0	8.0	10	11.4
Adsorption time (min) (C)	26	50	85	120	144

Desorption step
Independent variables	Sanges and levels
	− α	− 1	0	+ 1	+ α
Eluting solvent volume (μL) (A)	59	100	200	300	341
Desorption time (s) (B)	16	30	65	100	114

Statistical evaluation of adsorption step

Analysis of Variance (ANOVA) was employed to rigorously assess experimental data obtained. As seen in Table 2, model p-value was less than 0.0001 (i.e., < 0.05), which suggested an unequivocal demonstration of the statistical significance of the model at a confidence level of 95%. In the developed model, terms with p-values greater than 0.05 were statistically not significant and, by implication, were considered to have no contribution towards the predictive capability of the model. The analysis revealed that all linear terms and binary interaction terms were statistically non-significant, whereas all quadratic terms were significantly prominent. The retention of prominent quadratic terms was therefore justified in the model, whereas non-significant binary interactions were selectively dropped to refine the predictive framework.

Table 2.

ANOVA’s table for CCD (central composite design).

Adsorption step
Source of variance	Sum of squares	Degree of freedom	Mean squares	F-value	p-value
Papaverine
Model	995.73	8	124.47	463.18	< 0.0001
Residual	2.96	11	0.2678
Lack-of-fit	0.7876	6	0.1313	0.3027	0.9110
Pure error	2.17	5	0.4337
R2 = 0.9970	Adj. R2 = 0.9949	Pred. R2 = 0.9926	Adeq Precision = 59.5775

Desorption step
Source of variance	Sum of squares	Degree of freedom	Mean squares	F-value	p-value
Papaverine
Model	3267.34	5	653.47	324.99	< 0.0001
Residual	14.08	7	2.01
Lack-of-fit	10.86	3	3.62	4.51	0.0899
Pure error	3.21	4	0.8030
R2 = 0.9957	Adj. R2 = 0.9926	Pred. R2 = 0.9749	Adeq Precision = 50.0542

Besides, “lack of fit” statistic of the model was determined as 0.9110 (> 0.05), indicating that lack of fit was not significant. This result is an indication of the power and adequacy of the model in predicting the experimental results³⁰.

Taking only the statistically significant terms into consideration, the second-order fitted model for determination of targeted analyte underneath optimum extraction conditions is expressed as follows:

3

The tight agreement between the coefficient of determination (R²) of 0.9970, the adjusted R² of 0.9949, and the predicted R² of 0.9926 indicates the excellent predictive accuracy of the model, showing that the experimental design very accurately represents the empirical trends in the data³¹.

Furthermore, diagnostic plots—like normal probability plot of residuals (Fig. 2a), the correlation betwixt actual and predicted values (Fig. 2b), and residuals versus run number analysis (Fig. 2c)—all show the good distribution of data within acceptable statistical limits. These graphical analyses also augment the good agreement between model prediction and experimental values, as well as the absence of systematic bias, in attesting to the validity and reliability of the methodology herein.

Fig. 2.

The normal probability plot (a), predicted and actual values (b), the analysis of residuals in relation to run number (c), Response surfaces for papaverine drug as analyte in absorption stage: sample solution’s pH versus sorption time (d); sorbent dosage vs. sorption time (e).

Interactive effects of variables in sorption To describe interactive effects of critical extraction parameters, three-dimensional response surface plots were used for their ability to show the correlation between extraction recovery and pairs of independent variables with all other variables constant at their optimized values. As shown in Fig. 2d, the three-dimensional surface plot shows the synergistic interaction between pH of sample solution and adsorption time on extraction efficiency of papaverine through the synthesized magnetic GO–MOF nanocomposite. The results clearly show that maximum extraction efficiency is achieved at approximately pH 8, which can be attributed to higher π–π interactions between papaverine molecules and functionalized nanocomposite surface. At elevated pH values, however, the greater density of the negative charge on the surface of the adsorbent inhibits these interactions, resulting in the adsorption efficiency being reduced. Moreover, the maximum adsorption time was determined to be 90 s, being the point at which equilibrium is established between the analyte and the available active sites on surface of adsorbent. Extended adsorption times led to decreasing efficiency of extraction, presumably due to disruptive actions of ultrasonic vibration, with a possibility of desorption of the analyte from the nanocomposite surface.

Three-dimensional response surface illustrating the impact of adsorbent dosage and adsorption time on extraction performance is presented in Fig. 2e. Results indicate that efficiency of extraction increases with augmenting adsorbent mass up to 8.5 mg, representing an increase in exposed active sites and, consequently, enhanced uptake of the analyte. For higher masses, the extraction efficiency decreases due to particle clumping and the corresponding reduction in effective surface area exposed to interact with the analyte.

The optimal preconcentration and determination conditions of papaverine in the adsorption phase were found with the desirability function method. As per this multi-criteria optimization, the best conditions were pH 8, 8.5 mg dosage of adsorbent, and 90 s contact time of adsorption. At the high papaverine concentration used for CCD optimizations (200 ppb), with these optimized conditions, the highest extraction efficiency of 74.35% was achieved with a desirability value of 1.0, which is a guarantee of the stability and robustness of the developed method.

Statistical evaluations of de-sorption stage

Prior to exploitation of CCD in optimizing three significant variables during de-sorption stage, selection of a proper solvent for elution was systematically examined using the one-variable-at-a-time approach. Elution solvent choice is an important aspect of desorption in extraction protocols, with its ability to functionally disrupt interactions between the analyte and adsorbent surface essentially determining extraction process efficiency. Desorption effectiveness greatly improves where the solvent has maximum compatibility with the analyte in terms of polarity and structural characteristics, thereby allowing for maximum analyte recovery and separation³².

In this study, a comparative evaluation was performed based on six solvents of varying polarities—methanol, ethanol, acetonitrile, chloroform, and two binary mixtures of solvents (ethanol/methanol and ethanol/acetonitrile, both 1:1 v/v). As indicated by Fig. 3a, methanol, being very polar in nature, performed more efficiently in the extraction of papaverine, and therefore was selected as the optimum eluent for further experimental procedures.

Fig. 3.

Type of elution solvent (a), the normal probability plot (b), the analysis of residuals in relation to run number (c), predicted and actual values (d) Response surfaces for papaverine drug in de-sorption step: De-sorption time versus Eluent volume (e).

Upon solvent selection, other variables influencing the desorption process were further optimized through RSM (response surface methodology). A CCD was utilized in particular to systematically investigate and optimize the effects of eluent volume (μL) and desorption time (s). The experimental design consisted of 13 runs that included 4 factorial points, 4 axial points, and 5 center points. Table 1 presents levels of studied variables and the respective experiment responses.

The importance of the principal effects, their interactions, and model fit were explored stringently by analysis of variance (ANOVA). Results of ANOVA for desorption step, presented in Table 2, were compared for the preconcentration and determination of papaverine, with model significance calculated at 95% confidence level using p-values, as done for the adsorption step.

The quadrupole response surface equation derived for the desorption stage is as follows:

4

where the coefficients are the effects of respective variables and their interaction. The extremely high R² (0.9957), adjusted R² (0.9926), and predicted R² (0.9749) values for papaverine confirm the feasibility, accuracy, and predictability of suggested model. Besides, diagnostic plots including normal probability plot (Fig. 3b), predicted against actual (Fig. 3c), and residuals against run number (Fig. 3d) indicate acceptable distribution of residuals, excellent agreement between predicted and observed, and absence of systematic error in the procedure.

Collectively, these findings demonstrate the reliability and validity of optimized model and confirm its feasibility for successful preconcentration and determination of papaverine in complex biological matrices.

Interactive effects of de-sorption variables Fig. 3e is a three-dimensional response surface plot describing the effect of the major parameters on the efficiency of desorption of the analyte during the UA-D-μ-SPE process using the magnetic GO-MOF nanocomposite. It is indicated that efficiency of extraction of analyte increases with the increase in volume of the eluent up to a maximum of 220 µL. Deviation from this maximum volume in the downward or upward direction results in reduced efficiency because of, respectively, insufficient desorption from the sorbent surface with smaller eluent volumes and dilution with larger volumes. Similarly, greater elongation of desorption time enhances the recovery of analytes, as indicated by the desorption percentage to achieve its maximum at 65 s. Briefer desorption durations are not sufficient for complete extraction of the analyte from adsorption active sites and hence limit the desorption process³³. Raising the period of the desorption process beyond 65 s, however, has a negligible reduction in efficiency, due to potential re-adsorption of the analyte partially onto the sorbent surface.

By applying the desirability function, the best conditions for preconcentration and determination of papaverine at the desorption stage were strictly established. The best parameters—a desorption time of 65 s combined with an eluent volume of 220 µL—yielded a maximal 97.61% extraction efficiency supported by an almost perfect desirability value of 0.999.

Reusability of solid phase material

The nanocomposite material used herein possesses a several useful properties, including superior chemical and physical stability, economic feasibility, simplicity of synthesis, and high reusability. To further investigate the operational stability of the material under stringent conditions, the UA-D-μ-SPE process was conducted in a systematic manner for eight consecutive cycles using model solutions of papaverine at a concentration of 200 ng mL⁻¹. Following each high-performance liquid chromatography (HPLC) run, peak areas achieved were closely compared to the preceding runs. The nanosorbent was taken through a regeneration process after each extraction cycle, involving two successive washes with 2 mL of acetonitrile and a final wash with water. The finding of this research had no observable change in the chromatographic peak areas throughout the successive uses. Besides, relative standard deviations achieved after eight extraction cycles were all less than 5%, demonstrating the excellent reproducibility and stability of the suggested nanocomposite material.

Adsorption isotherms

The adsorption isotherm study forms an integral cornerstone in the analysis of surface activity, providing profound knowledge of the processes governing the distribution of the adsorbate species between phases solid and liquid³⁴. An adsorption isotherm represents the relationship between the amount of adsorbate adsorbed with respect to one unit of adsorbent weight as a function of its concentration in equilibrium in solution at a constant temperature³⁵. These isotherms therefore are of immense benefit in comprehending the equilibrium characteristics of such systems. Through this analysis, one not only explains the nature of interfacial interaction of adsorbent and adsorbate but also gains useful parameters such as adsorption capacity, binding constant, surface homogeneity, and the fundamental physical or chemical cause of the adsorption phenomenon³⁶.

Several theoretical models have been developed to explain adsorption behavior, of which the Langmuir and Freundlich isotherms are outstanding. The Langmuir model envisions monolayer coverage of a homogeneously structured surface that has uniform binding sites, while the Freundlich model addresses multilayer adsorption on energetically heterogeneous surfaces made of non-uniform active sites³⁷.

The Langmuir equation is given by:

5

where q_e represents the equilibrium adsorption capacity (mg g⁻¹), C_e is concentration of analyte at equilibrium (mg L⁻¹), q_max represents maximum capacity of adsorption (mg g⁻¹), and b_L is a constant showing affinity of the adsorption process.

The Freundlich isotherm, on the other hand, can be represented as:

6

where q_e and C_e keep their original meanings, n is a measure of heterogeneity of the energies of binding, and k_F is Freundlich constant related to strength of adsorption.

Figures 4a, b illustrate Freundlich and Langmuir linearized isotherms for papaverine adsorption. Determination coefficients, coupled with calculated adsorption capacities, were used to critically test and compare the predictive accuracy of the two models to describe the equilibrium data. Experimental findings indicated that the Langmuir model gave a superior correlation coefficient (R² = 0.9991) compared to Freundlich model (R² = 0.9940), suggesting a better fit of the latter to the equilibrium data. In addition, the calculated maximal sorption capacity of 57.82 mg g⁻¹ using Langmuir model was in good agreement with the experimentally obtained maximal capacity value for papaverine (57.06 mg g⁻¹) Overall, these findings evidently confirm that adsorption largely takes place by monolayer coverage on fixed homogeneous sites of the prepared adsorbent as assumed by the Langmuir model.

Fig. 4.

Langmuir (a) and Freundlich (b) isotherms, pseudo-second-order (c) and pseudo-first-order (d) kinetic models for papaverine drug (at initial concentration of 200 ng mL⁻¹); HPLC chromatogram obtained for human plasma sample: blank chromatogram (e1) and sample solution chromatogram in concentration of 20 ng mL⁻¹ (e2), both after applying proposed method.

Adsorption kinetics

The adsorption kinetics plays a central role in explaining both nature and complexity of surface-dependent processes as well as mass transfer phenomena³⁷. It is one of the three pillars employed in assessing and optimizing the performance of adsorption systems. Through extensive specific kinetic measurements, it is achievable to arrive at significant parameters specific to the reaction rate, the time required to reach equilibrium, and the principal mechanistic processes, either physical or chemical in nature³⁶. Through such comprehensive analysis, a better and subtle explanation of the processes involved in the interactions betwixt adsorbent and adsorbate can be given.

To quantify and interpret the complex nuances of adsorption kinetics, many mathematical models have been formulated, among which two famous ones include the pseudo-first-order, and pseudo-second-order. The pseudo-first-order kinetic model is typically applied in situations where physical adsorption is prevalent, whereas pseudo-second-order model more aptly describes processes governed by chemisorption or physiochemical forces—where stronger, more specific interactions prevail.

In this case, kinetic adsorption was studied in a systematic manner to elucidate mechanistic pathways through which the target drug interacts with the magnetic GO-MOF nanocomposite, as illustrated in Fig. 4c, d. The experimental kinetics data were strictly subjected to both pseudo-first-order and pseudo-second-order models. Findings conclusively indicate that papaverine adsorption onto the GO-MOF magnetic nanocomposite is best described by pseudo-second-order kinetic model, which gave a satisfactory correlation coefficient of R² = 0.9962, in strong contrast with the comparatively very low R² value of 0.9656 given for pseudo-first-order model. Moreover, the pseudo-second-order-calculated adsorption capacity (18.94 mg/g) was consistent with the experimentally determined q_e value (18.24 mg/g) obtained at the tested concentration (i.e., 200 ng ml⁻¹), which further supports its applicability and justifies the validity of the suggested mixture of physiochemical forces responsible for papaverine adsorption on the synthesized nanocomposite, viz. π-π interactions, hydrogen bonding, electrostatic forces, etc.³⁷.

Analytical performance

The analytical validation of the developed method was critically set through rigorous assessment of important performance parameters, including linearity, intra- and inter-day precision, reproducibility, and limits of detection (LOD) and quantification (LOQ), all underneath well-optimized experimental conditions³⁸. The thus-obtained calibration plots of papaverine were found to be perfectly linear over a concentration span of 0.3 to 40 ng mL⁻¹, as indicated by a correlation coefficient > 0.999, which again confirms the robust quantitative capability of the method. Computation of detection and quantification limits was done according to conventional signal-to-noise rations of 3:1 and 10:1, respectively. Therefore, the lowest concentration which is detectable (LOD) of papaverine was determined to be 0.09 ng mL⁻¹, whereas LOQ was also found at 0.3 ng mL⁻¹, indicating the high sensitivity of method and its suitability for trace analysis.

Accuracy of the method was also scrutinized using repeated measurements across intra-day and inter-day cycles, giving relative standard deviations (RSDs) of 3.3% and 4.7%, respectively, as shown in Table 3. These results are a reflection of the high methodological reproducibility built into the methodology. The method also gave a Enrichment factor (EF) of 44.37 for the analyte, which is another attestation to its effectiveness in analyte detectability enhancement. In general, such results strongly support quality and deservingness of approach, rendering it of high value for rigorous quantitative quantification of papaverine in complex biological matrices.

Table 3.

Analytical performance of the proposed method.

Analyte	LODa	LDRb	R2c	RSD%d (n = 5) Intra-day (Inter-day)	ER%e	EFf
Papaverine	0.09	0.3–40.0	0.9991	3.3 (4.7)	97.61	44.37

^aLimit of detection (ng mL⁻¹).

^bLinear dynamic range (ng mL⁻¹).

^cCoefficient of determination.

^dRelative standard deviation for 10 mL of solution with 10 ng mL⁻¹ of each analyte.

^fEnrichment factor.

Analysis of real samples

The potentiality of the new ultrasonic-assisted dispersive micro-solid phase extraction (UA-D-μ-SPE) technique, based on the use of a magnetic graphene oxide–metal–organic framework (GO-MOF) nanocomposite, was extensively demonstrated by papaverine extraction from complex biological matrices—i.e., plasma, saliva, urine, and breast milk—prior to high-performance liquid chromatography (HPLC) analyses. For this purpose, actual biological samples were intentionally spiked with known quantities of papaverine at two different concentrations (10.0 and 20.0 ng mL⁻¹), thereby enabling a comprehensive investigation of analytical performance of method underneath regular working conditions. Notably, as demonstrated in Table 4, endogenous papaverine could not be detected in all the matrices examined, demonstrating the selectivity of the method. Interestingly, relative standard deviation (RSD) values were all below 3.1%, highlighting the exceptional precision and reproducibility of this technique. Recoveries for the spiked analyte always fell within the strict limit of 95.24–98.08%, which highlights the efficiency and dependability of the method across a broad range of biological textures. Moreover, Fig. 4e is a side-by-side comparison of chromatograms from unspiked and spiked plasma samples, visually verifying the effective extraction and detection of papaverine from challenging sample matrices. Overall, the results strongly verify the UA-D-μ-SPE method, demonstrating its reliable use for the sensitive and accurate quantitation of trace drugs in biological samples—a functionality enabled by the synergistic action of the magnetic GO-MOF sorbent in combination with careful optimization of extraction conditions.

Table 4.

Analytical results for papaverine drug in real samples with the devised method.

Real sample	Caddeda	Cfoundb	RR%c (RSD%)
Human breast milk	–	N Dd	–
	10	9.56	95.67 (2.8)
	20	19.36	96.83 (2.5)
Plasma	–	N D	–
	10	9.52	95.24 (3.1)
	20	19.32	96.62 (2.7)
Urine	–	N D	–
	10	9.64	96.42 (2.6)
	20	19.47	97.38 (2.1)
Saliva	–	N D	–
	10	9.71	97.11 (2.3)
	20	19.61	98.08 (2.0)

^aSpiked concentration (ng mL⁻¹).

^bConcentration of analytes (ng mL⁻¹) in the sample after spiking analytes.

^cRelative recovery percentage.

^dNot detected.

Comparison with literature

An intensive comparative evaluation of the turning point benefits of the newly prepared microextraction technique against other methods of quantitative analysis of the targeted focus pharmaceutical compound is presented in Table 5. The results therein largely establish that the proposed method stands apart due to its unparalleled speediness, directly attributed to very effective mass transfer processes facilitated within the system. When contrasted with other recent microextraction techniques, nevertheless, the new strategy possesses significant advantages in the guise of its much lower limit of detection (LOD) and acceptable relative standard deviation (RSD), thereby offering higher sensitivity and reproducibility.

Table 5.

Comparison of the devised method with other published methods.

Extraction method	Nanoadsorbent	Analyte	LOD (ng mL−1)	RSD%	References
MEPS-HPLC-UV1	Covalent organic framework-polypyrrole- cetyltrimethylammonium bromide (COF-PPy-CTAB) nanocomposite	Papaverine	0.1	4.4	27
On-chip-SPE-UV-Vis2	Polyurethane/polyaniline (PU/PANI) nanofibers	Papaverine	0.3	5.7	39
ESI-UPLC-MS/MS3	–	Papaverine	0.3	9.8	40
MSPE-LC-QqQlit-MS/MS4	Mesostructured silica magnetic composite with β-CD (Fe3O4@SiO2@mSiO2@β-CD)	Papaverine	50.0	5.8	41
UA-D-μ-SPE-HPLC5	Magnetic graphene oxide-metal–organic frameworks (GO-MOF) nanocomposite	Papaverine	0.09	3.3	This work

¹Microextraction in a packed syringe-high performance with an ultraviolet detector.

²On-chip integrated microfluidic device for solid-phase extraction with ultraviolet detector.

³Electrospray ionization-ultra performance liquid chromatography coupled with tandem mass spectrometry.

⁴Magnetic solid-phase extraction-liquid chromatography coupled to quadrupole linear ion trap mass spectrometry.

⁵Ultrasound-assisted dispersive micro solid phase extraction using high-performance liquid chromatography.

Its excellent analytical performance is also further boosted by the careful employment of a pioneering nanocomposite, characterized by its strong sorptive capacity and optimum responsiveness. The synergistic integration of this innovative material with ultrasound irradiation is to substantially accelerate extraction kinetics and procedural efficiency. Collectively, these innovations result in a protocol not only better than the earlier set ones (as attested by Table 5) for analytical performance and ease of operation but also representing a visionary concept in pharmaceutical sample preparation. Hence, the virtues of this approach are in its coalescence of speed, sensitivity, and unyielding accuracy, presenting a breakthrough in the field.

Conclusion

In conclusion, this study efficiently proposes a novel, green, and highly effective ultrasound-assisted dispersive micro-solid phase extraction (UA-D-μ-SPE) method with a magnetic graphene oxide/metal–organic framework (GO/MOF) nanocomposite as the major adsorbent for trace enrichment and extraction of papaverine in complex biological matrices. The GO/MOF nanocomposite, specifically developed and found through a range of advanced techniques (like FT-IR, FE-SEM, XRD, EDS, VSM, and BET), showed impressive specific surface area, improved adsorption, excellent chemical and heat stability, and satisfactory reusability. The magnetic nature of the sorbent greatly aided phase separation without the need for centrifugation and hence greatly reduced operational complexity and environmentally friendly analytical concepts. Method development was sequentially enhanced by Central Composite Design (CCD), allowing experimental runs and chemical use to be minimized whilst at the same time delivering highly effective analytical performance—defined by an extremely low limit of detection (LOD) and limit of quantification (LOQ) of 0.09 ng mL⁻¹ and 0.3 ng mL⁻¹, respectively, and excellent intra- and inter-day precision (3.3% and 4.7% at 10 ng mL⁻¹, respectively). Besides, the method showed superb recoveries (95.24–98.08%) in actual biological matrices, asserting its applicability in challenging matrices. The combination of this UA-D-μ-SPE methodology with HPLC–UV detection not only enhances sensitivity and selectivity but also reduces sample preparation time and operational expense. Overall, these advances affirm the method’s promise as an eco-friendly, high-performance, and efficient method for trace-level pharmaceutical analysis, paving new ways for biomedical and clinical research applications.

Author contributions

S.Z. did data collection, and writing original draft; S.A.-B. did investigation, methodology, and supervision; A.B.R. did investigation and formal analysis; M.R. did writing—review and editing, supervision, and project administration; A.A. did conceptualization, methodology development, and writing—reviewing and editing; F.J.S.-V. did software, and writing—review and editing; and A.H.-B. did conceptualization, Methodology development, and writing—reviewing and editing.

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed consent was obtained from all human participants involved in the study prior to sample collection, in accordance with ethical standards and institutional guidelines.