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In-vial temperature gradient headspace single drop microextraction designed by multi-physics simulation Sharmin Jahan, Qiang Zhang, Amit Pratush, Haiyang Xie, Hua Xiao, Liu-Yin Fan, and Cheng-Xi Cao Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02514 • Publication Date (Web): 07 Oct 2016 Downloaded from http://pubs.acs.org on October 10, 2016
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In-vial temperature gradient headspace single drop microextraction designed by multi-physics simulation Sharmin Jahan, † Qiang Zhang, † Amit Pratush, Haiyang Xie, Hua Xiao,* Liuyin Fan, and Chengxi Cao*1 Laboratory of Analytical Biochemistry and Bioseparation, State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
* Corresponding Authors': Dr. Hua Xiao, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. Fax: +86 21-3420 5682, email address:
[email protected]. * Corresponding Authors': Dr. Chengxi Cao, Fax: +86-21-3420 5820, E-mail:
[email protected]. † The first two authors have equal contribution to the work. Sharmin Jahan conducted the design of project and experiments, and organized the work, while Qiang Zhang developed the micro-device and mathematic model of TG-HS-SDME, and performed the numerical simulation. 1
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Abstract Presented herein was a novel headspace single drop microextraction (HS-SDME) based on temperature gradient (TG) for on-site preconcentration technique of volatile and semi-volatile samples. Firstly, an inner vial cap was designed as a cooling device for acceptor droplet in HS-SDME unit to achieve fast and efficient microextraction. Secondly, for the first time an in-vial TG was generated between the donor phase in sample vial at 80oC and the acceptor droplet under the inner vial cap containing cooling liquid at -20°C for a TG-HS-SDME. Thirdly, a simple mathematic model and numerical simulations were developed by using heat transfer in fluids, Navier-Stokes and mass balance equation for conditional optimization and dynamic illumination of the proposed extraction based on COMSOL Multiphysics®. Five chlorophenols (CPs) were selected as model analytes to authenticate the proposed method. The comparisons revealed that the simulative results were in good agreement with the quantitative experiments, verifying the design of TG-HS-SDME via the numerical simulation. Under the optimum conditions, the extraction enrichments were improved from 302 to 388 folds within 2 minutes only, providing 3.5 to 4 times higher enrichment factors as compared to a typical HS-SDME. The simulation indicated that these improvements in the extraction kinetics could be attributed due to the applied temperature gap between sample matrix and droplet within the small volume of headspace. Additionally, the experiments demonstrated a good linearity (0.03~100 µg/L, R2>0.9986), low limit of detection (7~10 ng/L) and fair repeatability (80℃) water vapors and high pressures inside the sample vial interfered with extraction, leading to the inferior precision and droplet instability. Therefore, 80℃ was chosen as a safe sample matrix temperature for further studies. Subsequently, the temperature of cooling liquid was specifically examined over the range of -20℃ to 20℃. The concentrations of chlorophenols were enhanced by 7
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more than 3 fold with declining the temperature of cooling liquid from 20℃ to -20℃ (Figure 2D). The experiments were approximately fitted to the calculated value of Panel B, demonstrating the simulation. Thus, cooling liquid of -20℃ was selected. As it was challenging to measure the actual temperature of acceptor droplet practically, we simulated the virtual model of the experimental condition in different temperature and try to realize the real temperature of acceptor droplet. As shown in Figure S3, the temperature of acceptor droplet was found ~ 25℃ at 2 min extraction time when -20℃ anti-freeze liquid was applied. However, the droplet temperature was more than 35℃ and 45℃ if using anti-freeze liquid at 0℃ and water at 20℃ as cooling liquid respectively. The temperature of droplet seemed higher than the expected perhaps due to the very tiny air space in the micropipette channel playing as an insulator between cooling liquid and droplet. No ice particle formation was observed during the extraction process when -20℃ to 0℃ cooling liquid was flowed inside the inner cap lumen. For attaining higher and rapid extraction rate we chose -20℃ anti-freeze liquid for our developed method.
Increment of Extraction Efficiency and Speed via In-vial TG-HS-SDME. Figure 3 unveiled the comparison of extraction yields of chlorophenol with and without using cooling systems. Three experimental conditions were selected where the extraction procedure using water at room temperature (20 ℃) and air instead of antifreeze liquid inside the lumen of cooling device consider as without cooling system and typical HS-SDME respectively. It was observed in the simulation of Panel A that the extraction yields of the chlorophenols with cooling system increased very fast, the extraction equilibrium was reached in ~3 min. The extraction yield with cooling system was approximately 3~3.5 times more than the one without cooling system during 2~3 min of extraction time. The experiments in Panel B and (Figure S4) revealed (i) the rapid increase of all five chlorophenol extractions with cooling system in ~2 min runs; (ii) the slow increment after 3 min SDME with cooling system; and (iii) gradually little increment in response area of extraction with water or air at room temperature. These experiments were reasonably consistent with the simulative results in Panel A, manifesting the extraction kinetics and condition design by the numerical simulation. From Figure 3A and S3, it could be assumed that the temperature gap played a key role for extraction if all other parameters were considered as constant. Furthermore, several parameters that affect the extraction efficiency were explored via the in-vial TG-HSSDME, such as acceptor phase and its volume (Figure S5), donor phase and their variables (Figure S6). There were two probabilities; the water vapor can be condensed on the surface of the hydrophilic solvent droplet at the low temperature and the evaporation of acceptor phase due to high headspace temperature. Therefore, the effect of water vapor on the size of extraction solvent droplet was examined and found that no or negligible volume reduction was occurred with using cooling system (Figure S5B). The actual reason of this phenomenon was not completely clear. It was assumed that small amount of water vapor was condensed on the acceptor droplet, whereas negligible volume of organic solvent was evaporated. However, from the extraction efficiency it could be said that water vapor has little effect on extraction. Finally the following optimum 8
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conditions were selected in line with the extraction yield (viz., the peak areas of chromatograms): 7 µL methanol (60%) as the extracting solvent, 3.5 mL sample solution (pH 2.0) added with 10% w/v NaCl as the donor phase, -20℃ anti-freeze liquid inside the inner vial cap, 2 min extraction time and 80℃ extraction temperature of sample. Figure 4 (Panel A, B and C) displayed the typical chromatograms for 50 µg/L chlorophenols spiked in water sample extraction with and without applying cooling system. The LC-UV signal representing the good enrichment of chlorophenols within 2 min by the in-vial TG-HS-SDME that could not be attainable in 5 min by proceeding extraction without using cooling liquid inside the inner vial. The rapid and efficient microextraction with cooling liquid might be accredited to significant temperature gap between the sample matrix and acceptor droplet within the small headspace of vial. This phenomenon could be explained as there was the change of surface tension of droplet due to different temperature within the interfacial space when cooling liquid was used. The change of surface tension trigged Marangoni effect that was responsible for the fluid flow inside the droplet and mass transfer, too.34,35 Moreover, mass transfer was expected to be more rapid in the headspace as diffusion coefficient in gas phase was characteristically ten thousand times greater than that of the condensed phase.8 Temperature gradient within the small headspace increased velocity of gases fluid flow around the droplet and facilitated the mass transfer of analyte from headspace to acceptor droplet (details are given in section “Comprehend High Efficiency of TG-HS-SDME by Simulation”). Decline of response area in LC-UV from 4 to 5 min indicated the exothermic nature of chemical extraction process of headspace for all five analytes in the typical HS-SDME (Figure 3 and Figure S4). Moreover, the simulation was little bit underestimated in terms of extraction yields from the experimental results, and the equilibrium time of ~2 min in the experiments was faster than that of mathematical modeling. As plausible explanation it could be said that there might be other parameters involved in real scenario that were not exactly reflected in our present numerical modeling.
Validation of Method. A series of experiments were conducted by spiking the chlorophenols in water sample to evaluate the developed method regarding of the linearity, limits of detection (LODs), reproducibility, and enrichment factors (EF) (Table 1). Good linearity was obtained for each chlorophenol over the range of 0.03-100 µg/L as shown by the correlation coefficients (R2 >0.9986). The relative standard deviations (RSDs) of lower than 5.9% (n=6) demonstrated the good reproducibility of the proposed method for the extraction of chlorophenols. The LODs based on a signal-to-noise ratio (S/N) of 3 ranged from 0.007–0.01 µg/L, greatly lower than those of previous report on headspace SDME (0.2 µg/L) and hollow fiber (0.4 µg/L),36 and even better than direct immersion solvent bar microextraction (0.02 µg/L).37 The EF was calculated as the ratio between the analyte concentration in the acceptor droplet measured by LC-UV and the concentration of the analyte in the donor phase. The comparisons of enrichment factors in in-vial TG-HS-SDME with typical HSSDME were presented in Table 1. Our microextraction method exhibited more than 3.5 fold sensitivity than typical method. 9
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Besides the advantages mentioned above, the developed microextraction had the following merits. First, an attractive characteristic of the microextraction was its suitability for volatile reagent as acceptor phase without obvious volume loses, offering a general application approach. Second, the microextraction could be applied for on-site analysis of volatile or semi-volatile compound due to no requirements of large instrument (e.g., liquid CO2 or N2 cylinder). Third, solvent evaporation always occurred in classic SDME and limited its use to GC. To get over the difficulties, ionic liquid (IL) was used for SDME. However, its greater viscosities, low volatilities and limited water solubility make IL incompatible for direct injection of GC. Our proposed method addressed the issue of solvent volatility making this procedure auspicious in wide variety of instrumental analyses of volatile and semi-volatile compounds in complex sample matrix. Such research is in progress in our lab.
Real Sample Analysis. To validate the proposed method in real sample analysis, seawater, red wine, honey and organic tomato samples spiked with five chlorophenols at two concentration levels (5 and 50 µg/L) were analyzed (Table 2). In non-spiked real samples, no target analytes were detected via the developed method prior to analysis. Relative recoveries of the extraction were calculated as the ratio of the response area after extraction from real samples and the ultra-pure water following the same condition. As summarized in Table 2, relative recoveries of above 81.6% and RSDs of less than 8.7% were obtained for all analytes, representing that the established method is applicable to real sample analysis. In addition, the LODs were set less than 100 ng/L for wine, 10 ng/L for water sample and 1.0 ng/g for food samples, and our developed method complied the standards and was suitable for the determination of chlorophenols at trace level concentrations in environmental water and food as well as beverage samples. However, a little lower relative recovery was found in honey sample as compared to other three samples perhaps the viscous nature of honey interfered in heat transfer and release of analyte, leading to low extraction efficiency. To validate the accuracy, student’s t-test was applied to the experimental results reveal acceptability within the 95% confidence level. Figure 4 (Panel D, E and F) displayed the chromatogram of extraction yield of spiked (50.0 µg/L of each chlorophenols) real samples via the developed method, illustrating the aptness in diverse real samples.
Comprehend High Efficiency of TG-HS-SDME by Simulation. The following numerical simulations could be useful for comprehending the extraction process without implementation of multiple experiments. The asymptotic simulations were focused onto: (i) temperature distribution, (ii) fluid flow, and (iii) mass transfer in acceptor droplet and headspace in microextraction unit. Heat Transfer and Fluid Flow in TG-HS-SDME. Firstly, we revealed two physical modules of heat transfer and fluid flow inside the microextraction unit by numerical simulation (Figure 5A and B). It was noticeable in Panel A and Figure S7 that the heat transfer occurred from the bottom to the top of gas phase; there was higher temperature near the liquid phase, however the temperature around the droplet was ranging 10
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from 20 to 50 ℃ owing to the cooling system holding the droplet at 22-25℃ in 1-2 min. It was apparent in Panel B and Figure S8 that the heat transfer could induce fluid flow disturbance and the fluid flow in the confined space likewise contribute to the heat transfer. Due to higher temperature gap between sample matrix and droplet, the heat transfer from liquid phase to headspace triggered flow field disturbance. It was observed that the gas phase at the bottom close to the wall of vial first rose to the vial top and migrated towards the droplet and then after extraction quickly the gases goes down because of the low temperature around the droplet cooled by the cooling system. The velocity of fluid flow was higher below the droplet. Rapid and higher extraction might be due to the recurrent gases flow in circulating manner with high velocity that caused rapid contact of the volatile analyte to the acceptor droplet. Figure 5A and B further unveiled the temperature distribution and fluid velocity at 5 min within the droplet, respectively. Initially the droplet temperature was set at 20℃, but it sharply decreased at center point within 20 sec from 20℃ to below 10℃ and then ranged from 5℃ to 15℃ in 2 min followed by increasing temperature to ~20℃ and kept stable for next few minutes. In the droplet, the heat transfer from surface of the droplet into center after 2 min which was dominated by the headspace temperature. Figure 5B also illustrated the fluid flow model inside the droplet at 5 min. It was obvious that flow velocity was higher at the center of the droplet, causing a circulatory liquid flow inside, and improving the mass transfer of analytes from surface to the center of the droplet and the uniform mass distribution. Figure S7 and S8 demonstrated more details of heat transfer and liquid flow inside the headspace and droplet. Mass Transfer in TG-HS-SDME. Physical fields in the extraction could be divided into three parts, viz., thermal field, fluid field and mass transfer. Mass transfer of analytes from donor phase to acceptor phase depended on two factors of diffusion and convection. Within the present geometrical structure of TG-HSSDME, the convection played the principal role in whole mass transfer. The fast mass transfer was based on the fluid field caused by the thermal field as a driven force of efficient extraction. In addition, the heat and cooling parts met each other in the droplet. The mixed intensity of energy caused turbulence in the acceptor phase especially at the connect part of droplet and needle that easily conformed vortex quickly enriching large amount of analytes. It could be realized in Panel C that both diffusion and convection were apparent for mass transfer. Although convection was the critical factor for mass transfer, diffusion also played significant role in headspace as it was more than ten thousand times higher than acceptor phase. Due to high temperature of donor phase the target anlaytes were quickly transferred to headspace and concentrated into the droplet as a combined result of convection and diffusion. Based on the simulation, we considered Marangoni convection and diffusion as the leading driven forces for extraction. The concentration level was evidently higher in droplet as compared to headspace. The more featured data of mass transfer in droplet and headspace were shown in Figure S9. Conceivable Mechanism for High Efficacy TG-HS-SDME. The overall mass transfer in the extraction simulation could be illustrated by mass balance equation. Within the present multiphase system, the 11
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microextraction comprised interphase mass transfer where the analytes distribution in the system followed Herry’s law. Considering the present experiment, three phases involved in the extraction were donor phase at the bottom, gas phase at middle for transition and acceptor phase at the top of microextraction unit. The vial was heated by the water bath that caused the increase of temperature in the aqueous phase at first and then the heat transport to the gas phase. In these two phases, the temperature increased from room temperature to the setting value. The higher temperature acted as the driving force for fluid flow, and temperature differences in the space accelerated the fluid movement in different phases, aiding the migration of analytes to the interface easily and speeding up the mass transfer towards the acceptor phase. The temperature in the droplet was more complicated. Appearance of the fluctuations caused by heat and cold met within the droplet in first 2 min. Then more heat transferred into droplet and the temperature in the droplet rose slightly with the counteraction of cooling liquid. On decreasing the acceptor droplet temperature, TG-induced interfacial surface tension came into play. The heat transfer induced fluid flow according to Marangoni convection where the surface tension of liquid-air interface was depended on the concentration of a species, not the temperature distribution. However, the cooling system protected the acceptor droplet from the increases of temperature and fluid movement. This temperature gap was the critical factor for the intense fluid movement within the headspace and efficient transfer of analytes to droplet. Simply, Marangoni convection increased velocity of fluid flow induced by the heat and promoted the mass transfer. Obviously, the speed of mass transfer was mainly depended on the fluid convection.
CONCLUSIONS Herein, an inner vial cap was fabricated, and evaluated as a cooling device to create in-vial TG for fast and efficient sample pretreatment of HS-SDME. To the best of our knowledge, this was the first report of in-vial TG-HS-SDME using miniature cooling device. Multiphysics simulation was applied to comprehend the extraction efficiency. The comparisons between the simulating and experimental results demonstrated that the TG-HS-SDME could be precisely designed and illuminated based on the numerical simulation. Precise extraction conditions could be optimized via the numerical simulation as well as TG-HS-SDME runs. The enrichment factor of proposed in-vial TG-HS-SDME was 3.5~4.1 times higher than that of classical HSSDME. Commendable LOD (in the range of 7~10 ng/L), enrichment factor (up to 388), and satisfactory repeatability (RSD
