Simultaneous determination of oxygen and pH inside microfluidic devices using core-shell nanosensors

A powerful online analysis setup for the simultaneous detection of oxygen and pH is presented. It features core–shell nanosensors, which enable contactless and inexpensive read-out using adapted oxygen meters via modified dual lifetime referencing in the frequency domain (phase shift measurements). Lipophilic indicator dyes were incorporated into core–shell structured poly(styrene-block-vinylpyrrolidone) nanoparticles (average diameter = 180 nm) yielding oxygen nanosensors and pH nanosensors by applying different preparation protocols. The oxygen indicator platinum(II) meso-tetra(4-fluorophenyl) tetrabenzoporphyrin (PtTPTBPF) was entrapped into the polystyrene core (oxygen nanosensors) and a pH sensitive BF2-chelated tetraarylazadipyrromethene dye (aza-BODIPY) was incorporated into the polyvinylpyrrolidone shell (pH nanosensors). The brightness of the pH nanoparticles was increased by more than 3 times using a light harvesting system. The nanosensors have several advantages such as being excitable with red light, emitting in the near-infrared spectral region, showing a high stability in aqueous media even at high particle concentrations, high ionic strength, or high protein concentrations and are spectrally compatible with the used read-out device. The resolution for oxygen of the setup is 0.5–2.0 hPa (approximately 0.02–0.08 mg/L of dissolved oxygen) at low oxygen concentrations (<50 hPa) and 4–8 hPa (approximately 0.16–0.32 mg/L of dissolved oxygen) at ambient air oxygen concentrations (approximately 200 hPa at 980 mbar air pressure) at room temperature. The pH resolution is 0.03–0.1 pH units within the dynamic range (apparent pKa 7.23 ± 1.0) of the nanosensors. The sensors were used for online monitoring of pH changes during the enzymatic transformation of Penicillin G to 6-aminopenicillanic acid catalyzed by Penicillin G acylase in miniaturized stirred batch reactors or continuous flow microreactors. Miniaturized laboratory systems (lab-on-a-chip technologies) using microfluidic devices are useful tools for various scientific and industrial fields such as cell culturing, (1) organ-on-a-chip applications, (2) biomedical research, (3, 4) or organic synthesis. (5) Human individuals around the world benefit from progress in microfluidics even if the appropriateness of lab-on-a-chip technologies has to be reassessed for some applications. Developments such as diagnostic devices for low resource environments, (3) more physiologically relevant in vitro models, (4) high throughput cell screening methods, (6) or chemical synthesis procedures under harsh reaction conditions (5) are now available due to the advancements in microfluidic technologies. Faced with these advances, however, analytical chemists are challenged to find new methods to determine analytes accurately within lab-on-a-chip devices. (7) The use of luminescent sensors can be one useful technique for obtaining online and real-time analytical data of small molecules (ions, glucose etc.) or process parameters (pH, oxygen and temperature) at this miniaturized scale. Luminescent sensors are highly suited for microfluidic applications due to their high sensitivity, ability of contactless read-out, ease of miniaturization, availability of various sensor formats (e.g., integrated sensor films, nanoparticles etc.) and their low cost. (8-10) However, efforts are necessary to develop sensor and read-out systems for lab-on-a-chip applications which are reliable, simple to use, easily accessible, and in the best case capable of multianalyte detection. A recent review covers most of the developments in the field of integrated luminescence sensors within microfluidic devices, (10) and two other reviews specialized on optical oxygen sensors give an additional insight into other sensor formats, for example, oxygen-sensitive nanoparticles. (8, 9) In a newer report, Poehler et al. (11) showed the integration of a fluorescent pH sensor layer into a microfluidic chip for continuous electrophoretic separation of biomolecules via free flow isoelectric focusing. Further, Grist et al. (12) recently designed a microfluidic device with integrated ratiometric oxygen sensors for controlling the oxygen concentrations within the chip for long-term cell culture applications. Recently, we demonstrated the application of optical oxygen sensors in a microfluidically perfused three-dimensional human liver model (13) and in microfluidic droplets for cell culture experiments. (14) Moreover, we recently presented two novel techniques for the integration of optical oxygen sensors into glass-based microfluidic devices and introduced two miniaturized read-out devices based on luminescent lifetime measurements in the frequency domain (phase shifts) for lab-on-a-chip applications. (15, 16) Because of our recent developments and gained knowledge of luminescence sensing in microfluidic devices, we aimed to develop a measurement system which is able to simultaneously determine the key process parameters pH and dissolved oxygen (DO). Therefore, we decided to use a frequency-domain luminescence lifetime-based method referred to as modified dual lifetime referencing (m-DLR). (17, 18) This method relies on measurement of the overall phase shift of two luminescence indicators with largely different luminescent lifetimes (e.g., pH and oxygen indicator) at two different modulation frequencies. We refer the reader to a recent review of time-resolved luminescence determination methods for further information about different techniques. (19) The m-DLR method is a self-referencing technique which only determines relative contributions between the two luminescence indicators; therefore, it is less prone to errors compared to simple intensity based methods. A recent mini-review summarizes the current state of the art of pH and DO sensors that are applied to bioprocesses from microfluidic systems to benchtop scale. (20) To our knowledge no m-DLR system for simultaneously sensing of pH and oxygen in microfluidic devices was presented so far in the literature and only few macroscopic investigations for simultaneously sensing of pH and DO based on m-DLR techniques are described in the literature. (21, 22, 18) In addition, all former studies are based on sensor films. Hence, we investigated a m-DLR setup for the simultaneous determination of oxygen and pH in microfluidic devices based on core–shell nanosensors and miniaturized oxygen meters. (15) The presented system offers the advantage over former reports that it uses red light excitation, emits in the near-infrared, which causes less background fluorescence or scattering from chip materials or biological matter, possesses higher photostability and uses nanosensors, which can be used in microfluidic devices without an integration step in comparison to sensor films. The poly(styrene-block-vinylpyrrolidone) core–shell nanosensors are ideally suited for different applications due to their low toxicity to cells and high stability in aqueous media even if high particle concentrations (20 mg/mL), high ionic strength, or high protein concentrations are used. Moreover, the two different polymeric domains of the particles have complementary properties, therefore allowing their use as oxygen and pH nanosensors. The in-house prepared oxygen and pH indicator dyes show high brightness (BS = ε × quantum yield) are excitable with red light and emit in the near-infrared, a spectral region where fewer compounds emit. Moreover, the dyes are very lipophilic which enables physical entrapment inside the nanoparticles and are compatible with the used read-out device. Further, a light harvesting system was applied in order to enhance the brightness of the pH nanosensors which additionally allows ratiometric imaging. We applied this setup for real-time monitoring of pH changes during the enzymatic transformation of Penicillin G to 6-aminopenicillanic acid catalyzed by Penicillin G acylase in microreactors. Experimental Section ARTICLE SECTIONSJump To Materials Platinum(II) meso-tetra(4-fluorophenyl) tetrabenzoporphyrin (PtTPTBPF), 2,3-bis(3,3,9,9-tetramethyl-2-hydroxyjulolidine)but-2-enedinitrile (Schiff base), and BF2-chelated tetraarylazadipyrromethene dye (aza-BODIPY) were synthesized in-house according to the literature procedures (for chemical structures see Figure 1). (23-26) For preparation of the Zn-Schiff base see the Supporting Information. Figure 1 Figure 1. Chemical structures of the oxygen indicator (PtTPTBPF), the antenna dye (Zn-Schiff base), and the pH indicator (aza-BODIPY). Poly(stryrene-block-1-vinylpyrrolidone) (PSPVP, 38% dispersion in water, consist of 64% w/w of styrene and 36% w/w of vinylpyrrolidone and are <500 nm in size), sodium dihydrogen phosphate (≥99.0%, anhydrous), glucose oxidase from Aspergillus niger, and zinc acetate dihydrate were purchased from Sigma-Aldrich. Poly(vinylidene chloride-co-acrylonitrile) (PViCl-PAN, 20% w/w polyacrylonitrile, 125 000 g/mol) was bought from Scientific Polymer products, Inc. (scientificpolymer.com). Penicillin G acylase (PGA) from E. coli and Penicillin G (PenG) were a kind gift from Prof. Guisan (ICP-CSIC, Spain). Tetrahydrofuran (≥99.5% p.a.), acetic acid (100%, p.a.), sodium hydroxide, sodium sulphite, d-glucose, and buffer salts (CAPS and CHES) were obtained from Carl Roth (www.carl-roth.de). Sodium chloride and hydrochloric acid (1 M) were bought from VWR (Vienna, Austria). N-Methyl-2-pyrrolidone (99.5%, peptide grade) was purchased from ABCR (www.abcr.de). Ethanol (99.9%) was bought from AustrAlco (www.australco.at). Nitrogen, oxygen, and synthetic air (all of 99.999% purity) were obtained from Linde (Graz, Austria). Ultrapure water (Milli-Q) was used throughout the study. Preparation of PSPVP Nanoparticles for Oxygen Sensing PSPVP nanoparticle dispersion (526 mg of dispersion containing 200 mg of particles) was diluted with water (50 mL) and THF (30 mL). The mixture was stirred and THF (20 mL) containing PtTPTBPF (2 mg) was added dropwise. All THF and partly water were removed under reduced pressure to a final volume of approximately 20–30 mL. The particle concentration of the oxygen nanosensor stock dispersion was approximately 7–10 mg/mL. Preparation of PSPVP Nanoparticles for pH Sensing PSPVP nanoparticle dispersion (526 mg containing 200 mg of particles) was diluted with water (50 mL) and ethanol (40 mL). Aza-BODIPY dye (250 μL of a 1 mg/mL stock solution in THF, Figure 1) and Zn-Schiff base (500 μL of a 1 mg/mL stock solution in THF) were added to a solution of ethanol (20 mL) and this mixture was then added dropwise over 1 min to the nanoparticle dispersion while stirring. Ethanol and water were removed under reduced pressure to a final volume of approximately 13–20 mL. The particle concentration of the pH nanosensor stock dispersion was approximately 10–15 mg/mL. The same particles were also prepared without Zn-Schiff base. Also particles with different aza-BODIPY to Zn-Schiff base ratios were prepared accordingly (see the Supporting Information Table 1). Measurements MALDI-TOF mass spectra were recorded on a Micromass TofSpec 2E (further details see the Supporting Information). 1H and 13C spectra were recorded on a Bruker Avance III 300 MHz spectrometer at 300 (1H) and 75 (13C) MHz. Absorption spectra were measured at a Cary 50 UV–vis spectrophotometer (Varian). Emission spectra were acquired on a Hitachi model F-7000 fluorescence spectrometer (Japan) equipped with a red-sensitive photomultiplier R 9876 from Hamamatsu (Japan). The size of the beads was determined with a particle size analyzer Zetasizer Nano ZS (www.malvern.de) or with a transmission electron microscope (FEI Tecnai F20, further details see the Supporting Information). Light conditioning experiments were performed with an LED array containing 12 LEDs [OSRAM Oslon SSL 80 red (625 nm) on a round printed circuit board (www.LED-TECH.de)] at an irradiance of approximately 950–1150 W m–2. The pH of buffer solutions (acetate, phosphate, CAPS, and CHES each 40 mM) was adjusted with a pH meter (SevenCompact, Mettler-Toledo, Switzerland) equipped with a glass electrode (InLab Nano or Inlab Micro, Mettler Toledo, Switzerland). The glass electrode was calibrated with standard buffers of pH 4.01, pH 7.01, and pH 10.01 (Hanna Instruments, www.hannainst.com). The buffers were adjusted at constant ionic strength (IS = 0.30, 0.20, and 0.15 M) using sodium chloride as a background electrolyte. Two mass flow controller instruments (Read Y smart series) by Vögtlin Instruments (www.voegtlin.com) were used to obtain gas mixtures of defined oxygen partial pressures (pO2). Compressed air and nitrogen were used as calibration gases. The activity of soluble and immobilized Penicillin G acylase (PGA) was determined spectrophotometrically by using 6-nitro-3-phenylacetamide benzoic acid (NiPAB) as substrate and additionally by direct quantification of Penicillin G (PenG) hydrolysis (further details see the Supporting Information). Characterization of Nanosensors for the Simultaneous Determination of pH and Oxygen A dispersion containing a mixture of oxygen nanosensors (∼1.2 mg/mL) and pH nanosensors (∼5 mg/mL) was prepared. The dispersion was conditioned with light for 10 min prior to the characterization. The conditioned stock dispersion (0.5 mL) was diluted with water (0.5 mL) and buffer solution (1 mL, 40 mM of either acetate, phosphate, CAPS, or CHES, IS of either 0.30, 0.20, or 0.15 M) was added in order to obtain various pH calibration standards at different ionic strengths. The final pH of the calibration standards was determined with a pH electrode. Measurements were performed in glass vials at different temperatures or inside microreactors (channel width, 200 μm; channel depth, 400 μm; see the Supporting Information Figures 12 and 13) at room temperature (approximately 23 °C). The oxygen concentration within the calibration standards was adjusted by flushing them with defined gas mixtures of compressed air and nitrogen. Additionally, deoxygenated buffer dispersions were prepared by adding glucose oxidase and glucose or sodium sulphite to the dispersions. A temperature sensor connected to the temperature port of a FireStingO2 (PyroScience, Germany) was used to monitor the temperature during measurements. A modified optical oxygen meter (PiccolO2, PyroScience, Germany) including a stainless steel tube with an optical fiber (2 mm in diameter) was used for recording the phase shifts (luminescent lifetime measurements in the frequency domain) of the nanosensors. The phase shifts were recorded at two modulation frequencies (m1 = 2000 Hz, m2 = 8000 Hz, measurement duration 16 ms per frequency to enable dual parameter detection). A measurement software was provided from Pyro-Science. The optical fiber of the optical meter was adapted with a GRadient INdex (GRIN) lens (www.grintech.de) to enhance the signal intensities for measurements within microreactors by focusing the excitation light into the microchannel. The microreactors were connected via an in-house developed chip holder to syringe pumps (Cavro Centris pumps from Tecan, www.tecan.com), which were operated by a Lab VIEW program (www.ni.com/labview). Long-term measurements of light conditioned nanosensors (light conditioning for 10 min) were also performed to study the influence of the excitation light of the measurement device on the nanosensors; hence, more than 9000 measurement points (equals more than 150 min of continuous measurement at a sample interval of 1 measurement point per second) of two nanosensor samples (pH = 3.98 and 9.82) were recorded. Real-Time Monitoring of pH Changes during the Enzymatic Transformation of Penicillin G to 6-Aminopenicillanic Acid Catalyzed by Penicillin G Acylase Enzymatic transformations were performed in glass vials (1 mL) or inside a microreactor. Calibration standards (40 mM of either acetate, phosphate, CAPS, or CHES, IS either 0.30 M) as mentioned above were prepared. The calibration of the nanosensors was performed at different pH values, at ambient air oxygen concentrations, and at room temperature. A nanosensor stock dispersion (250 μL, see above) was diluted with a phosphate buffer (200 μL, 50 mM, pH 7.5) containing 50 mM of Penicillin G (PenG) for enzymatic transformations inside glass vials. This mixture was further diluted with a phosphate buffer (500 μL, 20 mM, IS = 0.30 M, pH 8.2) and the pH was adjusted with strong base or acid to 8.3. The final dispersion was stirred and different amounts of Penicillin G acylase (PGA) were added. The final volume was 1 mL and the enzyme activities were 3.40, 2.88, 1.98, 1.20, and 0.36 U/mL (expressed as hydrolytic activity of PGA at the conditions referred to in the Supporting Information page S-2). The modified optical oxygen meter recorded the phase shifts of the nanosensors at two modulation frequencies (m1 = 2000 Hz, m2 = 8000 Hz) every 4 s. pH time courses were additionally simulated according to the known kinetic of PGA hydrolysis. The hydrolysis is catalyzed by the free enzyme where 1 mol of phenyl acetic acid is produced per mole of hydrolyzed PenG. (27) The dependency between enzymatic activity and pH exhibit a sigmoidal behavior and an apparent dissociation constant was experimentally determined (pK = 7.4) at the used conditions. (28) Two nanosensor dispersions were prepared for enzymatic transformations inside a microreactor (channel width, 200 μm; channel depth, 400 μm; volume, 22.7 μL). The first dispersion contained nanosensors (0.75 mL, mixture of oxygen nanosensors and pH nanosensors, see previous paragraph), phosphate buffer with PenG (0.60 mL, see above) and phosphate buffer (1.5 mL, 20 mM, IS = 0.30 M, pH 8.2). The second dispersion contained nanosensors (0.75 mL), water (0.664 mL), phosphate buffer (1.5 mL, 40 mM, IS = 0.30 M, pH 8.2) and PGA (81 μL). The enzyme activity was approximately 18 U/mL. Strong acid or base were added to the dispersion until the pH was adjusted to 8.0. The final volume of the dispersions was 3 mL. These two dispersions were introduced over the two main inlets into the microreactor at different flow rates (2.4, 1.2, 0.6, 0.3, and 0.1 μL/s). Measurements were performed with a modified optical oxygen meter equipped with a focusing lens. Results and Discussion ARTICLE SECTIONSJump To Measurement Device for Dual Parameter Detection in Microfluidic Applications Miniaturized dual-parameter detection tools are scarce for microfluidic applications. Hence, we investigated the use of a commercially available hardware (PiccolO2 with gradient index lens (15)) that was extended with a modified dual lifetime referencing (m-DLR) method (17) for this purpose. Two luminescent indicators with similar absorption and emission spectra but with largely different luminescent lifetimes are necessary for m-DLR measurements. Generally, an indicator that possess a relatively long lifetime in the range microseconds (phosphorescence of, e.g., an oxygen or temperature indicator) and an indicator that possesses a much shorter lifetime in the range of nanoseconds (fluorescence of, e.g., a pH or CO2 indicator) are used. Both dyes are excited by a sinusoidally modulated light source, and the superimposed overall luminescence emission and phase shift are recorded (Figure 2). The recording is performed at two modulation frequencies (e.g., 2000 and 8000 Hz in the case of PtTPTBPF) in order to obtain the luminescent intensity for each indicator and the luminescent lifetime of the longer living component. The mathematical derivation and further explanations can be found in the Supporting Information. The lifetime of the fluorophore which is in the nanosecond range cannot be determined by this setup. The fastest sampling rate (= time which is needed to measure and record at two different modulation frequencies) is 340 ms. The nanosensors are excited with a red light emitting diode (λmax = 620 nm) and the emission is detected by photodiode, which in combination with a long-pass filter collects the light in the near-infrared spectral region (λ > 700 nm). A gradient index lens focuses the excitation light to a point of approximately 80 μm in diameter to enhance signal intensities for microfluidic measurements. The measurement device in combination with our nanosensors (see Figure 2) was used for microreactors with channel dimensions of 200 μm in width and 400 μm in depth. Figure 2 Figure 2. Schema of the nanosensors and a nanosensor stock solution (A). m-DLR schema and the measurement device (B). The phase shifts (φ) and the amplitudes of the superimposed signals are used to obtain the luminescent intensity for each indicator and the luminescent lifetime of the phosphor. Nanosensors for the Simultaneous Determination of pH and Oxygen Dual sensing of pH and oxygen is challenging from a material point of view because a material for oxygen sensing should be permeable for oxygen but preferably impermeable to ions and protons which are necessary for pH sensing. We chose poly(styrene-block-vinylpyrrolidone) nanoparticles (average diameter = 180 nm) due to their core–shell structure (polystyrene core and hydrophilic polyvinylpyrrolidone shell) and their ability to integrate lipophilic indicator dyes into both domains. Furthermore, these particles can be easily prepared with high reproducibility and feature high stability in aqueous media. (29, 30) The nanoparticles are not prone to aggregation even at high particle concentrations (20 mg/mL), high ionic strength, or high protein concentrations such as Dulbecco’s modified Eagle medium. The size of the particles was investigated by TEM images (see the Supporting Information Figure 2) and dynamic light scattering experiments (z-av. 180 ± 5 nm in diameter, PDI 0.07 ± 0.02, n = 9). As shown previously, (30) lipophilic dyes can either be stained into the shell or the core of the particles by using two different procedures. Tetrahydrofuran/water mixtures can swell the polystyrene core which then enables incorporation of indicator dyes (e.g., oxygen indicator) into the core. Ethanol is used as a solvent in order to mainly entrap the indicator into the shell which is important for pH indicators since only the shell is permeable for protons. Here, platinum(II) meso-tetra(4-fluorophenyl) tetrabenzoporphyrin (PtTPTBPF) was used as an oxygen indicator and a BF2-chelated tetraarylazadipyrromethene dye (aza-BODIPY) was used as a pH indicator. Both dyes show excellent photostability, absorption in the red region of the electromagnetic spectrum, emission in the near-infrared, and tunable dynamic ranges. (31, 26) The spectral properties of the dyes are shown in the Supporting Information Figure 3. In principle, both dyes can be excited with the red LED (λmax approximately 620 nm) of the read-out instrument, but due to the fact that aza-BODIPY dyes show absorption maxima (acidic form) around 675 nm, a light harvesting system (32) with zinc(II) 2,3-bis(3,3,9,9-tetramethyl-2-hydroxyjulolidine)but-2-enedinitrile (Zn-Schiff base, λexc = 610 nm, λem = 650 nm) as an antenna dye was used to enhance the brightness. Indeed, the brightness was increased by a factor of 3 compared to nanosensors without the Zn-Schiff base antenna (see the Supporting Information Figure 4). No leaching was observed because all dyes are lipophilic. PtTPTBPF is stained into the polystyrene core, whereas aza-BODIPY and Zn-Schiff base are physically entrapped into the hydrophilic polyvinylpyrrolidone shell of the particle. Ideally, the three dyes are incorporated together into a single particle. However, preliminary experiments revealed that incorporation of both indicator dyes and the antenna dye into a single PSPVP particle yielded in unreproducible results which may derive from dye migration within the core and shell and interactions of the oxygen indicator with the two other dyes. Therefore, oxygen and pH nanoparticles are prepared separately and mixed together at a constant particle ratio to perform m-DLR measurements. In addition, oxygen and pH nanosensors can also be used separately from each other. The oxygen particles, for example, can be used with phosphorescence lifetime based measurement setups. The pH nanosensors can be used with intensity based ratiometric imaging setups because the antenna dye emits in the red and the pH indicator emits in the near-infrared spectral region. Characterization of the Nanosensors The dual pH/oxygen nanosensors were developed with regard to stability, reproducibility, accuracy, resolution, response time, cross sensitivity (temperature and ionic strength), and biocompatibility. We optimized the ratio of Zn-Schiff base to aza-BODIPY in order to obtain an efficient light harvesting system (see the Supporting Information Tables 1–3). Further, the ratio of oxygen nanosensors (1.2 mg/mL) to pH nanosensors (5.0 mg/mL) was adjusted to fine-tune the sensitivity of the m-DLR system. The pH/oxygen nanosensor stock solution was conditioned with light prior to the first use in order to obtain a more reliable sensor response. The response of unconditioned nanosensors changed during first exposure with light because of photochemical reactions with the polymeric material. After light conditioning, the nanosensors showed minimal change in the measurement signal (RSD < 0.5%, Supporting Information Figures 5 and 6) even if more than 9000 measurement points (equals more than 150 min of continuous measurement at a sample interval of 1 measurement point per second) were recorded by the measurement device. In addition, cytotoxicity screenings of pH and oxygen nanosensors were performed to rule out any detrimental effect of the nanosensors on cell viability (for experimental details see the Supporting Information). The metabolic activity of fibroblast cells was not affected even at the highest particle concentrations (1 mg/mL) with respect to the untreated control (see the Supporting Information Figure 14). Further, a cell viability assay based on Calcein AM and ethidium bromide showed that the intracellular esterase is active and that the plasma membrane of the cells is intact even after incubation overnight (see the Supporting Information Figure 15). Oxygen Measurements Measurements of the pH/oxygen nanosensors are performed at 2000 and 8000 Hz, and the superimposed signal intensities and phase shifts are recorded as described above. The determination of oxygen is straightforward because the luminescence lifetime of the oxygen indicator is directly calculated from dual frequency measurements and corresponded to the oxygen partial pressure (pO2) of the solution. A pH/oxygen nanosensor dispersion was purged with different oxygen/nitrogen gas mixtures and a Stern–Volmer calibration curve was obtained (f = 0.800 ± 0.015 and KSV = 17.1 ± 0.2 × 10–3 hPa–1 at 26 °C). A simplified two site model was used to fit the calibration data (eq 1). (33) The ratio I0/I in the model was replaced by τ0/τ, where τ is the lifetime of the oxygen indicator dye at a certain pO2-value. The lifetime τ0 represents the lifetime under deoxygenated conditions, where the oxygen indicator is in its unquenched state. A simple two point calibration procedure at air saturated and deoxygenated conditions is typically sufficient to achieve good accuracies.(1) The oxygen resolution of the measurement setup is 0.5–2.0 hPa at low oxygen concentrations (<50 hPa) and 4–8 hPa at ambient air oxygen concentrations (approximately 200 hPa at 980 mbar air pressure at room temperature). The nanosensors immediately respond to a change in oxygen concentrations and it is assumed that the response time is <1 s. The oxygen nanosensors are not influenced by variation of pH but show minor cross-sensitivity to temperature (decrease of τ0 and increase of KSV) as shown in Figure 3. Figure 3 Figure 3. Stern–Volmer calibration curves (left Y-axis) and luminescent lifetimes (right Y-axis) for different temperatures at pH 7.27 (A) and for different pH values at 21 °C (B). R2 > 0.99 for all shown calibration curves. pH Measurements Theoretically, the superimposed phase angle at 2000 or 8000 Hz, the signal intensity of the pH indicator or the ratio of the signal intensities of the pH indicator and of the oxygen indicator can be used for pH determination. The superimposed phase angle at 8000 Hz, however, offers the advantage that the signal is more stable and less prone to error compared to intensity based methods. Furthermore, the superimposed signal at 8000 Hz exhibits a higher pH resolution than the signal at 2000 Hz because of a larger phase angle difference between acidic and basic conditions. Hence, the superimposed signal at 8000 Hz was used for pH measurements. The cotangent of the phase angle at 8000 Hz was plotted against the pH as shown in Figure 4A for different oxygen concentrations. The cotangent φ shows linear dependency on the oxygen partial pressure (Figure 4B) if the temperature was kept constant. A Boltzmann sigmoid function was used to fit the pH calibration data (eq 2), where A2 is the bottom value, A1 is the top value, pKa′ (apparent pKa) is the point of inflection, and dx is the slope at the point of inflection.(2) Figure 4 Figure 4. pH calibration curves (A) at different oxygen concentrations at 26 °C (cotangent of superimposed signal at 8000 Hz, R2 > 0.99) and oxygen dependency of the calibration parameters A1 and A2 at different temperatures (B). We repeated the pH calibration at different oxygen concentrations with different nanosensor batches multiple times (n = 7) and observed for each sensor batch a similar behavior. A variation in oxygen concentration changed the values of A1 and A2, whereas, the other 2 calibration parameters (pKa′ and dx) stayed constant. The relative standard deviation (RSD) was <1% for pKa′ and <3% for dx. Therefore, a simplified calibration procedure for the oxygen/pH nanosensors typically consists of a pH calibration at air saturation and the determination of A1 and A2 at two different oxygen concentrations at minimum (e.g., air saturation and anoxic conditions). The pKa′ was 7.23 ± 0.1 and dx was 0.57 ± 0.01 for our system if the temperature was kept constant at 23 °C. It is essential for the simplified calibration model that the temperature is kept constant during the measurement in order to obtain reliable results. Another option to compensate the parameters A1 and A2 for different oxygen concentrations is to plot cotangent φ at the two plateau levels (e.g., pH < 4.5 → A1 or pH > 9.5 → A2) against the phase angle of the oxygen indicator (see the Supporting Information Figure 7). Therefore, both parameters are recorded at different oxygen concentrations. This can be done by using an oxygen depletion reaction, for example, glucose oxidase and glucose or sodium sulphite. The obtained curve can be fitted by an exponential decay function (exponential model) and shows the oxygen dependency of A1 and A2. In addition, the influence of different ionic strengths and temperatures on the pH nanosensors was investigated. A change in ionic strength from 75 mM to 150 mM resulted in a pH offset of approximately 0.2 pH units (near the pKa′). The influence of the temperature between 23 and 37 °C is even more pronounced, and therefore the temperature has to be constant to enable accurate measurements. The influence of the temperature and of the ionic strength on the pH calibration is illustrated in the Supporting Information Figures 8 and 9. The resolution of the pH nanosensors is 0.03–0.1 pH units if the pH is within the dynamic range (apparent pKa 7.23 ± 1.0) of the nanoparticles. The pH resolution is mostly influenced by the accuracy of the oxygen determination if the linear calibration model is used. The nanosensors showed the same calibration characteristics even 3 months after preparation (see the Supporting Information Figure 10). We evaluated the accuracy of oxygen and pH determination by using different particle solutions at known pH and oxygen concentrations. A1 and A2 were calculated by using the linear model (Figure 4B) and the exponential model. Both models showed similar results (see the Supporting Information Table 6). The measured pH values and oxygen concentrations were compared with the set O2 and pH values and show only minimal deviations (see Table 1). Further, the oxidation of glucose to d-glucono-δ-lactone and hydrogen peroxide by glucose oxidase was investigated to study the influence of a continuous change in oxygen on the pH measurement. Oxygen is depleted during the reaction which resulted in a pH independent increase of the measurement signals (phase angles). The pH of the solution was monitored by the nanosensors and additionally by a pH electrode. Both techniques showed similar pH values (see the Supporting Information Figure 11). The pH differences of both methods were lower than 0.1 pH units. The small pH increase of the nanosensors during the beginning of the enzymatic reaction possibly arise from slightly different oxygen concentrations during the 2000 and 8000 Hz measurements. Table 1. Evaluation of the Measurement Accuracy by Comparison of Known and Measured Values after Calibration sample pO2set/hPa pO2,meas./hPa ΔpO2/hPa pHSet pHmeas.a ΔpH 1 39.9 41.3 0.4 6.46 6.52 0.06 2 74.8 76.6 1.8 6.83 6.91 0.08 3 99.7 100.8 1.1 7.35 7.37 0.02 4 124.7 125.2 0.5 7.67 7.70 0.03 5 159.6 159.3 –0.3 7.97 8.02 0.05 a Calculation of A1 and A2 with the linear model. Furthermore, the system was applied in two different microreactors (see the Supporting Information Figures 12 and 13). The setup (100% LED intensity, 1.2 mg/mL pH nanosensors and 0.3 mg/mL oxygen nanosensors) provided sufficient signals (25–60 mV) with signal-to-noise ratios of 100–500 demonstrating the application in microfluidic devices. However, an in-chip calibration procedure is recommended for the achievement of accurate pH results. Real-Time Measurements of pH Course during the Enzymatic Transformation of Penicillin G to 6-Aminopenicillanic Acid Catalyzed by Penicillin G acylase The enzymatic transformation of Penicillin G (PenG) to 6-aminopenicillanic acid and phenyl acetic acid, which is catalyzed by Penicillin G acylase (PGA) was used as model reaction to test our measurement system. Initially, the pH time course was monitored by a pH electrode in the absence or presence of the nanosensors in order to test the potential perturbation on the enzymatic reaction. The reaction was performed in a stirred glass vial and no influence was detected (Supporting Information Figure 16). Therefore, the nanosensors (particle concentrations were 1.25 mg/mL for pH and 0.3 mg/mL for oxygen) can be used in the presences of soluble enzyme catalysts. Further, the setup was used to measure the pH time course at different concentrations of enzyme catalyst. The nanosensors could determine the initial pH irrespectively of the catalyst concentration and the modulation of the pH time course promoted by different hydrolysis velocities of PenG (Figure 5). The dynamic response of the nanosensors is fast enough for real-time monitoring of the characteristic acidification rates at different catalyst concentrations and the measured data fully agree with the expected values corresponding to simulations of PenG hydrolysis shown in Figure 5. The simulations only differed for the two highest enzyme concentrations at the end of the displayed reactions probably because of small deviations in the initial PenG and buffer concentrations. However, the pH end points were verified also by measurements with a pH electrode. Figure 5 Figure 5. The pH decrease measured by the nanosensors at different enzyme activities during the transformation of Penicillin G to 6-aminopenicillanic acid and phenyl acetic acid. Measurement data (n = 2) was compared with simulations (gray lines). 20 mM phosphate buffer, 10 mM Penicillin G, 150 mM ionic strength. Reaction was started by adding Penicillin G acylase. The biocatalytic transformation was also performed in a microreactor (Figure 6 and Supporting Information Figure 17) at different residence times. The residence time inside the microreactor (from the inlet to the measurement position) changed from 8.3 s, 16.6 s, 33.2 s, 66.4 s, to 199.3 s according to the flow rate (2.4, 1.2, 0.6, 0.3, and 0.1 μL/s). For each residence time, the pH at the measurement positions changed. These pH changes were recorded in triplicates (n = 3) and were 7.96 ± 0.03 (8.3 s), 7.89 ± 0.03 (16.6 s), 7.78 ± 0.04 (33.2 s), 7.67 ± 0.03 (66.4 s), and 7.59 ± 0.03 (199.3 s). The oxygen partial pressure stayed constant during the whole measurements (lifetime of the oxygen indicator was constant). The signal intensities (23–28 mV) of the nanosensors were sufficient with signal-to-noise ratios of 110–600. The peak drops in Figure 6 between the different residence times originate because the pumps stopped and refilled their syringes at these times, while the enzymatic reaction continued. Figure 6 Figure 6. Online monitoring during the enzymatic transformation of Penicillin G (PenG) to 6-aminopenicillanic acid and phenyl acetic acid by Penicillin G acylase (PGA) at different residence times (A) in the used microreactor (B). Data was used for the calculation of residence time dependent pH-values (n = 3) (C). Alternative Use of the Nanosensors Independent pH measurements can be also performed with oxygen insensitive PViCl-PAN nanoparticles (34) which were used instead of the described oxygen nanosensors (for preparation protocol see the Supporting Information). The PViCl-PAN particles were stained with PtTPTBPF and were mixed with pH nanosensors to obtain a dual lifetime referencing (DLR) system. The DLR-system has a higher pH resolution (theoretically approximately 0.01 pH values or below) and shows no cross sensitivity to oxygen because of the very low oxygen permeability of PViCl-PAN. Unfortunately, the particles suffer from several disadvantages. Preliminary experiments showed that PViCl-PAN nanosensors aggregate and precipitate at higher ionic strengths (≥150 mM). Aggregation and sedimentation was hindered by continuously stirring the dispersion. The PViCl-PAN nanosensors are also less photostable in comparison to the oxygen nanosensors and should be only used for short-term measurements. Calibration curves of the DLR-system in glass vials and in a microreactor are shown in the Supporting Information Figure 18. The pH nanosensors can be also applied in combination with a ratiometric imaging setup, for example, a 2-CCD color near-infrared camera. This application is possible because the antenna dye of the light harvesting system emits in the red channel and the pH indicator emits in the near-infrared channel of the camera. (35) A calibration curve of the pH nanosensors in a 96-well plate obtained by a 2-CCD color near-infrared camera is shown in the Supporting Information Figure 19. Conclusion ARTICLE SECTIONSJump To In summary, we report a powerful measurement setup consisting of NIR-emitting optical nanosensors and a read-out system based on commercially available oxygen meters that allow simultaneous sensing of pH and oxygen using a modified dual lifetime referencing algorithm. The setup is applicable in microfluidics and will be used in microfluidic droplet reactors, cell-based assays and lab-on-a-chip applications in the future. The nanosensors show high stability in aqueous media even at high particle concentrations, high ionic strength, or high protein concentrations. Additionally they do not show toxicity to cells and are photostable. The nanosensors were used for real-time monitoring of the pH time courses during enzymatic transformations. The measured pH time courses fully agreed with the expected values corresponding to simulations. Oxygen concentrations can be determined at a resolution of 0.5–8.0 hPa (approximately 0.02–0.32 mg/L of dissolved oxygen) depending on the oxygen levels. The pH value can be determined at a resolution of 0.03–0.1 pH units within the dynamic range (apparent pKa 7.23 ± 1.0) of the nanosensors. The pKa′ (7.2 ± 0.1) is in the physiological range which is an interesting region for various applications, e.g., cell based microfluidic assays or organs-on-chips. The system is of particular interest for microfluidic droplet applications because the nanosensors can be easily dispersed into media for monitoring and controlling the conditions within a droplet. In the future, we plan to develop a set of nanosensors with different dynamic ranges based on the aza-BODIPY pH indicator toolbox which was recently published. (26) Supporting Information ARTICLE SECTIONSJump To The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b02849. Additional experimental protocols, data, figures, and results (PDF) Simultaneous Determination of Oxygen and pH Inside Microfluidic Devices Using Core–Shell Nanosensors 16 views 0 shares 0 downloads Download figshare Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html. Author Information ARTICLE SECTIONSJump To Corresponding Author Torsten Mayr - Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, NAWI Graz, Stremayrgasse 9/3, 8010 Graz, Austria; Email: torsten.mayr@tugraz.at Authors Josef Ehgartner - Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, NAWI Graz, Stremayrgasse 9/3, 8010 Graz, Austria Martin Strobl - Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, NAWI Graz, Stremayrgasse 9/3, 8010 Graz, Austria Juan M. Bolivar - Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Petersgasse 12/1, 8010 Graz, Austria Dominik Rabl - Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, NAWI Graz, Stremayrgasse 9/3, 8010 Graz, Austria Mario Rothbauer - Institute of Applied Synthetic Chemistry, Vienna University of Technology, Getreidemarkt 9/163, 1060 Wien, Austria Peter Ertl - Institute of Applied Synthetic Chemistry, Vienna University of Technology, Getreidemarkt 9/163, 1060 Wien, Austria Sergey M. Borisov - Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, NAWI Graz, Stremayrgasse 9/3, 8010 Graz, Austria Notes The authors declare no competing financial interest. Acknowledgment ARTICLE SECTIONSJump To Financial support by the European Union FP7 Project BIOINTENSE–Mastering Bioprocess integration and intensification across scales (Grant Agreement Number 312148) is gratefully acknowledged. Further we thank Christoph Larndorfer who provided assistance with the mathematical derivation and Ilse Letofsky-Papst for the TEM images. References ARTICLE SECTIONSJump To This article references 35 other publications. 1El-Ali, J.; Sorger, P. K.; Jensen, K. F. Nature 2006, 442, 403– 411 DOI: 10.1038/nature05063 [Crossref], [PubMed], [CAS], Google Scholar 2Bhatia, S. N.; Ingber, D. E. Nat. Biotechnol. 2014, 32, 760– 772 DOI: 10.1038/nbt.2989 [Crossref], [PubMed], [CAS], Google Scholar 3Chin, C. D.; Linder, V.; Sia, S. K. Lab Chip 2007, 7, 41– 57 DOI: 10.1039/b611455e [Crossref], [PubMed], [CAS], Google Scholar 4Sackmann, E. K.; Fulton, A. L.; Beebe, D. J. Nature 2014, 507, 181– 189 DOI: 10.1038/nature13118 [Crossref], [PubMed], [CAS], Google Scholar 5Gutmann, B.; Cantillo, D.; Kappe, C. O. Angew. Chem., Int. Ed. 2015, 54, 6688– 6728 DOI: 10.1002/anie.201409318 [Crossref], [CAS], Google Scholar 6Guo, M. T.; Rotem, A.; Heyman, J. A.; Weitz, D. A. Lab Chip 2012, 12, 2146– 2155 DOI: 10.1039/c2lc21147e [Crossref], [PubMed], [CAS], Google Scholar 7Bolivar, J. M.; Wiesbauer, J.; Nidetzky, B. Trends Biotechnol. 2011, 29, 333– 342 DOI: 10.1016/j.tibtech.2011.03.005 [Crossref], Google Scholar 8Grist, S. M.; Chrostowski, L.; Cheung, K. C. Sensors 2010, 10, 9286– 9316 DOI: 10.3390/s101009286 [Crossref], Google Scholar 9Sun, Shiwen; Ungerböck, Birgit; Mayr, Torsten Methods and Applications in Fluorescence 2015, 3, 34002 DOI: 10.1088/2050-6120/3/3/034002 [Crossref], Google Scholar 10Pfeiffer, Simon A; Nagl, Stefan Methods and Applications in Fluorescence 2015, 3, 34003 DOI: 10.1088/2050-6120/3/3/034003 [Crossref], Google Scholar 11Poehler, E.; Herzog, C.; Lotter, C.; Pfeiffer, S. A.; Aigner, D.; Mayr, T.; Nagl, S. Analyst 2015, 140, 7496– 7502 DOI: 10.1039/c5an01345c [Crossref], Google Scholar 12Grist, S. M.; Schmok, J. C.; Liu, M. C.; Chrostowski, L.; Cheung, K. C. Sensors 2015, 15, 20030– 20052 DOI: 10.3390/s150820030 [Crossref], [CAS], Google Scholar 13Rennert, K.; Steinborn, S.; Groger, M.; Ungerbock, B.; Jank, Am; Ehgartner, J.; Nietzsche, S.; Dinger, J.; Kiehntopf, M.; Funke, H.; Peters, F. T.; Lupp, A.; Gartner, C.; Mayr, T.; Bauer, M.; Huber, O.; Mosig, A. S. Biomaterials 2015, 71, 119– 131 DOI: 10.1016/j.biomaterials.2015.08.043 [Crossref], Google Scholar 14Mahler, L.; Tovar, M.; Weber, T.; Brandes, S.; Rudolph, M. M.; Ehgartner, J.; Mayr, T.; Figge, M. T.; Roth, M.; Zang, E. RSC Adv. 2015, 5, 101871– 101878 DOI: 10.1039/C5RA20118G [Crossref], [CAS], Google Scholar 15Ehgartner, J.; Sulzer, P.; Burger, T.; Kasjanow, A.; Bouwes, D.; Krühne, U.; Klimant, I.; Mayr, T. Sens. Actuators, B 2016, 228, 748– 757 DOI: 10.1016/j.snb.2016.01.050 [Crossref], [CAS], Google Scholar 16Lasave, L. C.; Borisov, S. M.; Ehgartner, J.; Mayr, T. RSC Adv. 2015, 5, 70808– 70816 DOI: 10.1039/C5RA15591F [Crossref], [CAS], Google Scholar 17Borisov, S. M.; Neurauter, G.; Schroeder, C.; Klimant, I.; Wolfbeis, O. S. Appl. Spectrosc. 2006, 60, 1167– 1173 DOI: 10.1366/000370206778664590 [Crossref], [PubMed], [CAS], Google Scholar 18Vasylevska, G. S.; Borisov, S. M.; Krause, C.; Wolfbeis, O. S. Chem. Mater. 2006, 18, 4609– 4616 DOI: 10.1021/cm060967n [ACS Full Text ACS Full Text], Google Scholar 19Collier, B. B.; McShane, M. J. J. Lumin. 2013, 144, 180– 190 DOI: 10.1016/j.jlumin.2013.06.034 [Crossref], [CAS], Google Scholar 20Demuth, C.; Varonier, J.; Jossen, V.; Eibl, R.; Eibl, D. Appl. Microbiol. Biotechnol. 2016, 100, 3853– 3863 DOI: 10.1007/s00253-016-7412-0 [Crossref], Google Scholar 21Borisov, S. M.; Seifner, R.; Klimant, I. Anal. Bioanal. Chem. 2011, 400, 2463– 2474 DOI: 10.1007/s00216-010-4617-4 [Crossref], [PubMed], [CAS], Google Scholar 22Kocincova, A. S.; Nagl, S.; Arain, S.; Krause, C.; Borisov, S. M.; Arnold, M.; Wolfbeis, O. S. Biotechnol. Bioeng. 2008, 100, 430– 438 DOI: 10.1002/bit.21793 [Crossref], Google Scholar 23Borisov, S. M.; Saf, R.; Fischer, R.; Klimant, I. Inorg. Chem. 2013, 52, 1206– 1216 DOI: 10.1021/ic301440k [ACS Full Text ACS Full Text], Google Scholar 24Borisov, S. M.; Zenkl, G.; Klimant, I. ACS Appl. Mater. Interfaces 2010, 2, 366– 374 DOI: 10.1021/am900932z [ACS Full Text ACS Full Text], Google Scholar 25Jokic, T.; Borisov, S. M.; Saf, R.; Nielsen, D. A.; Kühl, M.; Klimant, I. Anal. Chem. 2012, 84, 6723– 6730 DOI: 10.1021/ac3011796 [ACS Full Text ACS Full Text], Google Scholar 26Strobl, M.; Rappitsch, T.; Borisov, S. M.; Mayr, T.; Klimant, I. Analyst 2015, 140, 7150– 7153 DOI: 10.1039/C5AN01389E [Crossref], Google Scholar 27Valencia, P.; Wilson, L.; Aguirre, C.; Illanes, A. Enzyme Microb. Technol. 2010, 47, 268– 276 DOI: 10.1016/j.enzmictec.2010.07.010 [Crossref], [CAS], Google Scholar 28Zahel, T.; Boniello, C.; Nidetzky, B. Process Biochem. 2013, 48, 593– 604 DOI: 10.1016/j.procbio.2012.12.003 [Crossref], [CAS], Google Scholar 29Borisov, S. M.; Herrod, D. L.; Klimant, I. Sens. Actuators, B 2009, 139, 52– 58 DOI: 10.1016/j.snb.2008.08.028 [Crossref], [CAS], Google Scholar 30Borisov, S. M.; Mayr, T.; Klimant, I. Anal. Chem. 2008, 80, 573– 582 DOI: 10.1021/ac071374e [ACS Full Text ACS Full Text], Google Scholar 31Borisov, S. M.; Nuss, G.; Klimant, I. Anal. Chem. 2008, 80, 9435– 9442 DOI: 10.1021/ac801521v [ACS Full Text ACS Full Text], Google Scholar 32Mayr, T.; Borisov, S. M.; Abel, T.; Enko, B.; Waich, K.; Mistlberger, G.; Klimant, I. Anal. Chem. 2009, 81, 6541– 6545 DOI: 10.1021/ac900662x [ACS Full Text ACS Full Text], Google Scholar 33Demas, J. N.; DeGraff, B. A.; Xu, W. Anal. Chem. 1995, 67, 1377– 1380 DOI: 10.1021/ac00104a012 [ACS Full Text ACS Full Text], Google Scholar 34Borisov, S. M.; Mayr, T.; Mistlberger, G.; Waich, K.; Koren, K.; Chojnacki, P.; Klimant, I. Talanta 2009, 79, 1322– 1330 DOI: 10.1016/j.talanta.2009.05.041 [Crossref], Google Scholar 35Ehgartner, J.; Wiltsche, H.; Borisov, S. M.; Mayr, T. Analyst 2014, 139, 4924 DOI: 10.1039/C4AN00783B [Crossref], Google Scholar Cited By This article is cited by 28 publications. W. Russ Algar, Melissa Massey, Kelly Rees, Rehan Higgins, Katherine D. Krause, Ghinwa H. Darwish, William J. Peveler, Zhujun Xiao, Hsin-Yun Tsai, Rupsa Gupta, Kelsi Lix, Michael V. Tran, Hyungki Kim. Photoluminescent Nanoparticles for Chemical and Biological Analysis and Imaging. Chemical Reviews 2021, 121 (15) , 9243-9358. https://doi.org/10.1021/acs.chemrev.0c01176 Stefanie Fuchs, Sofia Johansson, Anders Ø. Tjell, Gabriel Werr, Torsten Mayr, Maria Tenje. In-Line Analysis of Organ-on-Chip Systems with Sensors: Integration, Fabrication, Challenges, and Potential. 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Biotechnology and Bioengineering 2020, 117 (7) , 2046-2057. https://doi.org/10.1002/bit.27338 A.Yu. Alexandrovskaya, P.V. Melnikov, A.V. Safonov, A.O. Naumova, N.K. Zaytsev. A comprehensive study of the resistance to biofouling of different polymers for optical oxygen sensors. The advantage of the novel fluorinated composite based on core-dye-shell structure. Materials Today Communications 2020, 23 , 100916. https://doi.org/10.1016/j.mtcomm.2020.100916 Mario Rothbauer, Gregor Höll, Christoph Eilenberger, Sebastian R. A. Kratz, Bilal Farooq, Patrick Schuller, Isabel Olmos Calvo, Ruth A. Byrne, Brigitte Meyer, Birgit Niederreiter, Seta Küpcü, Florian Sevelda, Johannes Holinka, Oliver Hayden, Sandro F. Tedde, Hans P. Kiener, Peter Ertl. Monitoring tissue-level remodelling during inflammatory arthritis using a three-dimensional synovium-on-a-chip with non-invasive light scattering biosensing. 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Stern–Volmer calibration curves (left Y-axis) and luminescent lifetimes (right Y-axis) for different temperatures at pH 7.27 (A) and for different pH values at 21 °C (B). R2 > 0.99 for all shown calibration curves. Figure 4 Figure 4. pH calibration curves (A) at different oxygen concentrations at 26 °C (cotangent of superimposed signal at 8000 Hz, R2 > 0.99) and oxygen dependency of the calibration parameters A1 and A2 at different temperatures (B). Figure 5 Figure 5. The pH decrease measured by the nanosensors at different enzyme activities during the transformation of Penicillin G to 6-aminopenicillanic acid and phenyl acetic acid. Measurement data (n = 2) was compared with simulations (gray lines). 20 mM phosphate buffer, 10 mM Penicillin G, 150 mM ionic strength. Reaction was started by adding Penicillin G acylase. Figure 6 Figure 6. Online monitoring during the enzymatic transformation of Penicillin G (PenG) to 6-aminopenicillanic acid and phenyl acetic acid by Penicillin G acylase (PGA) at different residence times (A) in the used microreactor (B). Data was used for the calculation of residence time dependent pH-values (n = 3) (C).