Microfluidic-Based Oxygen Sensors: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Vicky Zhou and Version 3 by Vicky Zhou.

Oxygen (O2) quantification is essential for assessing cell metabolism, and its consumption in cell culture is an important indicator of cell viability. Recent advances in microfluidics have made O2 sensing a crucial feature for organ-on-chip (OOC) devices for various biomedical applications. OOC O2 sensors can be categorized, based on their transducer type, into two main groups, optical and electrochemical.

  • oxygen sensors
  • microfluidics
  • organ-on-chips (OOCs)
  • on-chip monitoring

1. Introduction

Oxygen (O2) is one of the main components of cellular respiration and energy production [1]. The availability of O2 is a key metric that defines the pathway of adenosine triphosphate (ATP) generation and its resultant metabolites that serve as the living cell’s energy source [2][3]. In a high O2 environment, ATP is synthesized by the phosphorylation of the precursor molecule adenosine diphosphate (ADP). This process, thus called aerobic respiration, requires an adequate level of O2. It consists of the coupling of electron transport and oxidative phosphorylation, where O2 acts as the final electron acceptor from the oxidation of glucose and/or glycogen [4]. In low O2 environments, conversely, ATP is generated at an inefficient but rapid rate via a process called anaerobic glycolysis, where glucose and glycogen are metabolized to pyruvate and lactate in the absence of O2. This pathway is important in the functions of vital organs such as the kidney and retina as well as in tumor formation [5]. O2 availability determines the metabolic pathway that generates energy for cell function and survival and therefore is significantly important to measure for bioassays, cell culture and diagnostic applications.
Precise control of a small amount of fluid is possible inside microfabricated channels of microfluidic technology [6]. Integrated microfluidic chips are capable of performing highly sensitive and low-cost analyses. These platforms can be integrated with new technologies with cell culture/organoid studies at high temporal and spatial resolution. For instance, microfluidic platforms can quantitatively monitor cellular signals and cell secretions using well-developed cell-culture methods on microchips [7]. O2 measurement can be performed using sensors integrated into microfluidic chips for monitoring the metabolism and viability of cell, tissue, and organ [8][9]. Moreover, novel sensors embedded inside implantable microchips have recently been used for real-time in vivo oximetry in human or animal bodies [10]. Sensors can also be used to detect and quantify various analytes in a complex biological environment [11][12][13][14][15][16]. Therefore, a combination of integrated sensors is required for online and non-invasive monitoring of the cell intake, secreted metabolites, and microenvironment status for on-chip microfluidic studies such as OOC and bioreactors [12][13].
On-chip monitoring of O2 is pivotal in OOCs. The low concentration of O2 inside a small chip and its crucial biological role in cell metabolism and function would emphasize the need for its precise and selective quantification in the confined environment of a microfluidic channel or chamber [9][17]. With recent advances in microfluidics-based cell and tissue studies, such as OOC technologies, various sensors have been integrated into chips to monitor the microphysiological parameters of cells [12][18][19][20][21][22][23]. OOCs have the potential to better mimic human organs compared to the traditional in vitro models, and thus they can reduce the need for animal models in interventions such as the studies on drug efficacy and toxicity [24][25][26]. To model the function of many organs, such as the pancreas [27], brain [28][29][30], liver [31], vascular system [32], Gut [33], multiorgan approaches [34] and body-on-the-chip, OOC-integrated O2 sensing techniques have been used to culture cell monolayers, three dimensional (3D) cultures, spheroids, organoids and stem cells. They have also been used to model tumor microenvironment by mimicking the extracellular matrix (ECM) [35] and 3D culture of cancer cells [36][37].
Electrochemical and optical sensors are the main transducers for on-chip O2 monitoring [38], as they are precise, selective, and easy to miniaturize and implement inside chips [18][39]. In addition, micro and nanomaterials along with the innovative designs and polymers, and commercial readout devices are used for signal amplification to overcome the limitation related to the measurement of low O2 levels with electrochemical and optical sensors [13][40][41]. Here, we review the recent innovations in O2 sensors integrated into microfluidic chips, including OOC devices. We have categorized them based on their transducer type into two main sections, namely optical and electrochemical. We also discuss recent innovations and their advantages and disadvantages. Finally, we provide a comprehensive discussion of the current advances in the design, fabrication and application of optical and electrochemical O2 sensors.
Previous reviews have covered related topics such as oxygen control (2016) [9], general optical imaging and sensing (2014) [39], microfluidic OOC sensors (2010) [40], and other microphysiological sensors of OOCs [12][13][18][38]; however, there are no recent publications critically discussing the current advancements in OOC O2 sensing.

2. Oxygen Sensors in On-Chip Systems

Methods of on-chip O2 measurement can be categorized into two groups of sensors: (i) optical and (ii) electrochemical. Here, we highlight the application of these two types of sensors in on-chip studies, explain their mechanism of action, and discuss their advantages and disadvantages. Table 1 represents the summary of advantages and disadvantages of optical and electrochemical methods for on-chip O2 measurement.
Table 1. Comparison of chip-based electrochemical and optical O2 sensors.
Method Advantages Limitations
Table 2 represents the summary of most recent on-chip optical O2 sensors and their features, applied dyes, and their advantages.
Table 2. Characteristics of chip-based optical O2 sensors.
Optical O2 Sensor Application Dye Advantages References
EC-Based O2 Sensor LOD Advantages References
Optical
  • Precise, sensitive and selective
  • Easy to miniaturize
  • Nano and microparticles for dye protecting
  • Non-invasive, and contact-free
  • Easy to use and operate
  • Commercial dyes and read-out devices
  • Simple handling and sterilizing of the chip
  • Less need to recalibrate
  • Multiplex measurement in different chip spots
  • Simultaneously measure pH and metabolites
  • Complicated integration into the chip
  • Possible dyes bleaching
  • Sometimes needs microscope
Polystyrene chip, pore network structure, used solvent-induced fluorophore impregnation (SIFI) method for dye layer Cell
Platinum(II) tetrakis(pentafluorophenyl)porphyrin (PtTFPP) Enhanced sensitivity and stability, non-invasive, can be used for gas and dissolved O2 [41]
PDMS-container structure, and the glass substrate 105 cells/mL Short response time (6.9 s) [52]
Electrochemical
Poly (dimethylsiloxane) (PDMS) chip with glass layer coverage, applied oxygen gradient
  • Precise, sensitive and selective
  • Easily miniaturized/implemented inside chips
  • Nano and microparticles for dye protecting
  • Possible use of commercial read-out devices
  • Short response time
  • High sensitivity
  • Label-free
  • Several electrodes
  • Several surface modifications
  • Several designs and polymers can be used easily
Low-temperature co-fired ceramic (LTCC) in an improved Clark-type DO sensor
  • Invasive and consume oxygen
  • Expensive integration in the chip
  • Requires special instrument and skilled operators
 Liver

2.1. Optical Methods

Pt
(II) Up to 8.1 mg/LOctaethylporphine (PtOEP) Wide dynamic range, continuous measurement, non-invasive, worked in different flow rates easy fabrication, flexible configuration, short response time (10.9 s), real-time detection [53][42]
Cyclic olefin copolymer-based chip Lung PtTPTBPF Simultaneous O2 and pH, stop/flow measurements, long term stability (10 days), non-invasive [
pHEMA hydrogel layer with electrolyte and PDMS as gas-permeable membrane 0.121 μA cm−243]
μM−1 zero analyte consumption, 1-point calibration, long-term stability [54] PDMS chip, applied oxygen gradient Cancer PtOEPK Photostable, reusable, non-invasive [44]
PPy as the internal contact layer between polymeric sensitive membrane and gold 0.11 ± 0.02 mg L−1 Low cost, good performance and long-term potential stability [55] PDMS chip, silica microparticles Cancer Ru(dpp) Simple fabrication and handling, real-time, spatially-resolved measurements, low photobleaching, High sensitivity [45]
Poly (methyl methacrylate) (PMMA) chip, polystyrene microspheres Embryo study Pt-porphyrin Simultaneous O2 and pH, long-term measurement, highly sensitive for single embryo analysis [46]
Multi-sensor glass-chip with a PDMS imprinted microfluidic channel grid 100 pA per each 1% O2 Transparent for microscopic observation, cheap, high sensitivity [56]
Biocompatible glass chip fabricated using a hybrid thin film and laminate technologies 0.735 μA μM−1 cm−2 Low O2 consumption on the electrode, long-term stability [57] Poly (dimethylsiloxane) (PDMS) chip, polystyrene microbeads
Biocompatible PDMS biochip with Au/Nafion electrodesLiver ruthenium-phenanthroline (RuP) Every 15 min for 28 days measurement, without a decrease in signal loss and toxicity, simultaneous glucose and lactate measurements [ 50 mmol L−1 real-time and continuous O2 monitoring in dynamic flow conditions47]
[58] Glass chip, nanoparticle probes Stem cell platinum(II) meso-bis(pentafluorophenyl)bis(4-bromophenyl)porphyrin (PtTFPPBr2) Highly sensitive, real-time, label-free, high-intensity fluorescence emission, cell permeability [
Kapton tape with embedded spirally rolled Microchannels 12.89 nA mmHg −1 O248]
and temperature sensors, embedded spirally rolled microchannels [59] Teflon fluorinated ethylene propylene (FEP) tubing, poly(styrene-block-vinylpyrrolidone) nanobeads Bacteria Platinum (II)-meso-tetra(4-fluorophenyl)tetrabenzoporphyrin (PtTPTBPF) Minimized background fluorescence, simultaneous measurement, highly soluble and disperse nanobeads, prevents any interferences from biomolecules, short response times, no dye leaching, and long storage periods [49]
ElecCell technological platform using PVD 6 pA/s low-cost, easy to use and reproducible portable chip [60] Silicon/glass chip, core−shell nanosensors (poly(styrene-blockvinylpyrrolidone) Fibroblast cell PtTPTBPF Simultaneous O2 and pH, contactless and inexpensive read-out, high ionic strength, highly stable, online monitoring
ultra-microelectrode array (UMEA) 0.49 nAs−0.5/mg/L[50]
Ultra-short response time (<5 ms), 10 times lower O2 consumption [61] Glass chip, polymeric nanoparticles Cell Pt(II) benzoporphyrin Highly stable at different pH, ultrafast response (less than 0.2 s), no leaching, repeatable [51]

2.2. Electrochemical Methods

Table 3 summarizes the components, advantages and LOD of the selected ElectrochemicalC (EC) sensors for the measurement of DO.
Table 3. Characteristics of chip-based electrochemical O2 sensors.
Multi-planar SPE sensor coupled with cultivation cell wells
3 mg/L
Continuous long-term O
2
measurement, sensor reutilization
[
62]
Inkjet printing (IJP) DO sensors on the delicate porous substrate 28 ± 1 nA L mg−1 low O2 consumption on electrodes, short response time (60 s) [63]
Electrochemical microsensors combined with spheroid technology NM fast, precise, and continuous long-term measurement of metabolic directly in the microwell [64]
Spheroid on chip NM Real-time monitoring of metabolic activity and automated assays for toxicity evaluation [65]
NM: not mentioned.

3. Conclusions and Future Perspectives

Herein discussed recent advancements of oxygen sensors in on-chip systems and categorized them in two main groups: optical and electrochemical methods. The optical methods are reported to be more sensitive, easier to operate and cheaper compared to the EC methods. In most cases, they do not consume O2 during the process. In addition, they are compatible with commercially available luminescent dyes and optical readout devices, even fluorescent microscope which is convenient and available in most cell and tissue process centers. These sensors can be used for contactless monitoring by adding a sensing spot outside the chip readout or optical fiber, making the handling and sterilizing of the chip simpler. In addition, they do not need recalibration or experience decay over time. Compared with EC methods, therefore, these techniques are more commonly used in OOC applications. This is especially important for low concentrations of the sample when the stability and reusability of the sensor and its remaining intact are crucial. On the other hand, the EC methods have a shorter response time, and in most cases, a higher sensitivity than the previous group. In most cases, they can be used as label-free sensors, which again reduces the cost of sensor fabrication compared with optical ones. However, their integration in the chip is relatively expensive, complicated, and sometimes requires special relatively expensive instruments (Potentiostat/Galvanostat) and skilled operators.
In the future, the use of novel materials, fabrication techniques, and chemical/physical surface modifications can help facilitate the fabrication steps, reducing the price and required specialty, making the chip-integrated O2 sensors more affordable. Although there are few examples of micro- and nanomaterials used for the chip-based O2 sensors of either type, the field is rapidly progressing. It is expected that a variety of materials with various properties to help with O2 sensing will be used for these sensors in the near future to improve their sensitivity. This is mainly because these materials are believed to increase the active surface area, enhance the stability of the surface and increase the glow of the dyes. For instance, graphene and quantum dots can be considered as potential materials due to their exceptional optical and electrochemical properties. Micro- and nanostructures of novel metal oxides are other examples of such materials because of their catalytic activity. In addition, quantum dots (QDs) are photostable and their excitement spectra are broad, while their emission spectra are size-tunable, which makes them suitable for optical sensors. Further, using a smartphone to quantify the emitted signal can significantly simplify the measurement process. Artificial intelligence and innovations in image and signal processing help enhance the sensitivity and specificity of O2 sensors. Additionally, using 3D printing to manufacture the sensors can result in flexible, rapid and low-cost designs and integration of sensors in OOCs, and improve the sensitivity of the O2 sensors inside the chips. Three-dimensional printing techniques can also be used to build customizable optical holders and modules that can be integrated with mobile phones or other portable detectors.

References

  1. Akram, M. Mini-review on Glycolysis and Cancer. J. Cancer Educ. 2013, 28, 454–457.
  2. Bonora, M.; Patergnani, S.; Rimessi, A.; De Marchi, E.; Suski, J.M.; Bononi, A.; Giorgi, C.; Marchi, S.; Missiroli, S.; Poletti, F.; et al. ATP synthesis and storage. Purinergic Signal. 2012, 8, 343–357.
  3. Bennett, N.K.; Nguyen, M.K.; Darch, M.A.; Nakaoka, H.J.; Cousineau, D.; ten Hoeve, J.; Graeber, T.G.; Schuelke, M.; Maltepe, E.; Kampmann, M.; et al. Defining the ATPome reveals cross-optimization of metabolic pathways. Nat. Commun. 2020, 11, 4319.
  4. Salway, J.G. Metabolism at a Glance; Wiley: Hoboken, NJ, USA, 2016.
  5. Beckner, M.E.; Stracke, M.L.; Liotta, L.A.; Schiffmann, E. Glycolysis as primary energy source in tumor cell chemotaxis. J. Natl. Cancer Inst. 1990, 82, 1836–1840.
  6. Xiong, B.; Ren, K.; Shu, Y.; Chen, Y.; Shen, B.; Wu, H. Recent Developments in Microfluidics for Cell Studies. Adv. Mater. 2014, 26, 5525–5532.
  7. Spiller, D.G.; Wood, C.D.; Rand, D.A.; White, M.R.H. Measurement of single-cell dynamics. Nature 2010, 465, 736–745.
  8. McLean, I.C.; Schwerdtfeger, L.A.; Tobet, S.A.; Henry, C.S. Powering ex vivo tissue models in microfluidic systems. Lab Chip 2018, 18, 1399–1410.
  9. Oomen, P.E.; Skolimowski, M.D.; Verpoorte, E. Implementing oxygen control in chip-based cell and tissue culture systems. Lab Chip 2016, 16, 3394–3414.
  10. Kmiec, M.M.; Tse, D.; Mast, J.M.; Ahmad, R.; Kuppusamy, P. Implantable microchip containing oxygen-sensing paramagnetic crystals for long-term, repeated, and multisite in vivo oximetry. Biomed. Microdevices 2019, 21, 71.
  11. Moradi, V.; Akbari, M.; Wild, P. A fluorescence-based pH sensor with microfluidic mixing and fiber optic detection for wide range pH measurements. Sens. Actuators A Phys. 2019, 297, 111507.
  12. Ferrari, E.; Palma, C.; Vesentini, S.; Occhetta, P.; Rasponi, M. Integrating Biosensors in Organs-on-Chip Devices: A Perspective on Current Strategies to Monitor Microphysiological Systems. Biosensors 2020, 10, 110.
  13. Kieninger, J.; Weltin, A.; Flamm, H.; Urban, G.A. Microsensor systems for cell metabolism—From 2D culture to organ-on-chip. Lab Chip 2018, 18, 1274–1291.
  14. Gomez-Cruz, J.; Nair, S.; Manjarrez-Hernandez, A.; Gavilanes-Parra, S.; Ascanio, G.; Escobedo, C. Cost-effective flow-through nanohole array-based biosensing platform for the label-free detection of uropathogenic E. coli in real time. Biosens. Bioelectron. 2018, 106, 105–110.
  15. Rodoplu, D.; Chang, C.-S.; Kao, C.-Y.; Hsu, C.-H. A simple magnetic-assisted microfluidic method for rapid detection and phenotypic characterization of ultralow concentrations of bacteria. Talanta 2021, 230, 122291.
  16. Guo, S.; Stevens Corey, A.; Vance Tyler, D.R.; Olijve Luuk, L.C.; Graham Laurie, A.; Campbell Robert, L.; Yazdi Saeed, R.; Escobedo, C.; Bar-Dolev, M.; Yashunsky, V.; et al. Structure of a 1.5-MDa adhesin that binds its Antarctic bacterium to diatoms and ice. Sci. Adv. 2017, 3, e1701440.
  17. Zirath, H.; Rothbauer, M.; Spitz, S.; Bachmann, B.; Jordan, C.; Müller, B.; Ehgartner, J.; Priglinger, E.; Mühleder, S.; Redl, H.; et al. Every Breath You Take: Non-invasive Real-Time Oxygen Biosensing in Two- and Three-Dimensional Microfluidic Cell Models. Front. Physiol. 2018, 9, 815.
  18. Luka, G.; Ahmadi, A.; Najjaran, H.; Alocilja, E.; DeRosa, M.; Wolthers, K.; Malki, A.; Aziz, H.; Althani, A.; Hoorfar, M. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications. Sensors 2015, 15, 30011–30031.
  19. Li, X.; Soler, M.; Szydzik, C.; Khoshmanesh, K.; Schmidt, J.; Coukos, G.; Mitchell, A.; Altug, H. Single Cell Analysis: Label-Free Optofluidic Nanobiosensor Enables Real-Time Analysis of Single-Cell Cytokine Secretion. Small 2018, 14, 1870119.
  20. Errico, V.; Ninno, A.D.; Bertani, F.R.; Businaro, L.; Bisegna, P.; Caselli, F. Mitigating positional dependence in coplanar electrode Coulter-type microfluidic devices. Sens. Actuators B Chem. 2017, 247, 580–586.
  21. Li, X.; Soler, M.; Szydzik, C.; Khoshmanesh, K.; Schmidt, J.; Coukos, G.; Mitchell, A.; Altug, H. Label-Free Optofluidic Nanobiosensor Enables Real-Time Analysis of Single-Cell Cytokine Secretion. Small 2018, 14, 1800698.
  22. Podwin, A.; Lizanets, D.; Przystupski, D.; Kubicki, W.; Śniadek, P.; Kulbacka, J.; Wymysłowski, A.; Walczak, R.; Dziuban, J.A. Lab-on-Chip Platform for Culturing and Dynamic Evaluation of Cells Development. Micromachines 2020, 11, 196.
  23. Schmid, Y.R.F.; Bürgel, S.C.; Misun, P.M.; Hierlemann, A.; Frey, O. Electrical Impedance Spectroscopy for Microtissue Spheroid Analysis in Hanging-Drop Networks. ACS Sens. 2016, 1, 1028–1035.
  24. Rezaei Kolahchi, A.; Khadem Mohtaram, N.; Pezeshgi Modarres, H.; Mohammadi, M.H.; Geraili, A.; Jafari, P.; Akbari, M.; Sanati-Nezhad, A. Microfluidic-Based Multi-Organ Platforms for Drug Discovery. Micromachines 2016, 7, 162.
  25. Lee, S.H.; Hong, S.; Song, J.; Cho, B.; Han, E.J.; Kondapavulur, S.; Kim, D.; Lee, L.P. Microphysiological Analysis Platform of Pancreatic Islet β-Cell Spheroids. Adv. Healthc. Mater. 2018, 7, 1701111.
  26. Järvinen, P.; Bonabi, A.; Jokinen, V.; Sikanen, T. Simultaneous Culturing of Cell Monolayers and Spheroids on a Single Microfluidic Device for Bridging the Gap between 2D and 3D Cell Assays in Drug Research. Adv. Funct. Mater. 2020, 30, 2000479.
  27. Zbinden, A.; Marzi, J.; Schlünder, K.; Probst, C.; Urbanczyk, M.; Black, S.; Brauchle, E.M.; Layland, S.L.; Kraushaar, U.; Duffy, G.; et al. Non-invasive marker-independent high content analysis of a microphysiological human pancreas-on-a-chip model. Matrix Biol. 2020, 85, 205–220.
  28. Saleheen, A.; Acharyya, D.; Prosser, R.A.; Baker, C.A. A microfluidic bubble perfusion device for brain slice culture. Anal. Methods 2021, 13, 1364–1373.
  29. Gong, J.; Meng, T.; Yang, J.; Hu, N.; Zhao, H.; Tian, T. Three-dimensional in vitro tissue culture models of brain organoids. Exp. Neurol. 2021, 339, 113619.
  30. Samiei, E.; Seyfoori, A.; Toyota, B.; Ghavami, S.; Akbari, M. Investigating Programmed Cell Death and Tumor Invasion in a Three-Dimensional (3D) Microfluidic Model of Glioblastoma. Int. J. Mol. Sci. 2020, 21, 3162.
  31. Wang, Y.; Wang, H.; Deng, P.; Tao, T.; Liu, H.; Wu, S.; Chen, W.; Qin, J. Modeling Human Nonalcoholic Fatty Liver Disease (NAFLD) with an Organoids-on-a-Chip System. ACS Biomater. Sci. Eng. 2020, 6, 5734–5743.
  32. Tronolone, J.J.; Jain, A. Engineering New Microvascular Networks On-Chip: Ingredients, Assembly, and Best Practices. Adv. Funct. Mater. 2021, 31, 2007199.
  33. Marrero, D.; Pujol-Vila, F.; Vera, D.; Gabriel, G.; Illa, X.; Elizalde-Torrent, A.; Alvarez, M.; Villa, R. Gut-on-a-chip: Mimicking and monitoring the human intestine. Biosens. Bioelectron. 2021, 181, 113156.
  34. Zheng, L.; Wang, B.; Sun, Y.; Dai, B.; Fu, Y.; Zhang, Y.; Wang, Y.; Yang, Z.; Sun, Z.; Zhuang, S.; et al. An Oxygen-Concentration-Controllable Multiorgan Microfluidic Platform for Studying Hypoxia-Induced Lung Cancer-Liver Metastasis and Screening Drugs. ACS Sens. 2021, 6, 823–832.
  35. Valente, K.P.; Thind, S.S.; Akbari, M.; Suleman, A.; Brolo, A.G. Collagen Type I-Gelatin Methacryloyl Composites: Mimicking the Tumor Microenvironment. ACS Biomater. Sci. Eng. 2019, 5, 2887–2898.
  36. Marconi, A.; Quadri, M.; Saltari, A.; Pincelli, C. Progress in melanoma modelling in vitro. Exp. Dermatol. 2018, 27, 578–586.
  37. Fetah, K.L.; DiPardo, B.J.; Kongadzem, E.-M.; Tomlinson, J.S.; Elzagheid, A.; Elmusrati, M.; Khademhosseini, A.; Ashammakhi, N. Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration. Small 2019, 15, 1901985.
  38. Clarke, G.A.; Hartse, B.X.; Niaraki Asli, A.E.; Taghavimehr, M.; Hashemi, N.; Abbasi Shirsavar, M.; Montazami, R.; Alimoradi, N.; Nasirian, V.; Ouedraogo, L.J.; et al. Advancement of Sensor Integrated Organ-on-Chip Devices. Sensors 2021, 21, 1367.
  39. Wang, X.-D.; Wolfbeis, O.S. Optical methods for sensing and imaging oxygen: Materials, spectroscopies and applications. Chem. Soc. Rev. 2014, 43, 3666–3761.
  40. Grist, S.M.; Chrostowski, L.; Cheung, K.C. Optical Oxygen Sensors for Applications in Microfluidic Cell Culture. Sensors 2010, 10, 9286–9316.
  41. Grate, J.W.; Liu, B.; Kelly, R.T.; Anheier, N.C.; Schmidt, T.M. Microfluidic Sensors with Impregnated Fluorophores for Simultaneous Imaging of Spatial Structure and Chemical Oxygen Gradients. ACS Sens. 2019, 4, 317–325.
  42. Matsumoto, S.; Safitri, A.R.; Danoy, M.; Maekawa, T.; Kinoshita, H.; Shinohara, M.; Sakai, Y.; Fujii, T.; Leclerc, E. Investigation of the hepatic respiration and liver zonation on rat hepatocytes using an integrated oxygen biosensor in a microscale device. Biotechnol. Prog. 2019, 35, e2854.
  43. Müller, B.; Sulzer, P.; Walch, M.; Zirath, H.; Buryška, T.; Rothbauer, M.; Ertl, P.; Mayr, T. Measurement of respiration and acidification rates of mammalian cells in thermoplastic microfluidic devices. Sens. Actuators B Chem. 2021, 334, 129664.
  44. Orcheston-Findlay, L.; Hashemi, A.; Garrill, A.; Nock, V. A microfluidic gradient generator to simulate the oxygen microenvironment in cancer cell culture. Microelectron. Eng. 2018, 195, 107–113.
  45. Ando, Y.; Ta, H.P.; Yen, D.P.; Lee, S.-S.; Raola, S.; Shen, K. A Microdevice Platform Recapitulating Hypoxic Tumor Microenvironments. Sci. Rep. 2017, 7, 15233.
  46. Huang, S.-H.; Huang, K.-S.; Liou, Y.-M. Simultaneous monitoring of oxygen consumption and acidification rates of a single zebrafish embryo during embryonic development within a microfluidic device. Microfluid. Nanofluid. 2016, 21, 3.
  47. Ehrlich, A.; Tsytkin-Kirschenzweig, S.; Ioannidis, K.; Ayyash, M.; Riu, A.; Note, R.; Ouedraogo, G.; Vanfleteren, J.; Cohen, M.; Nahmias, Y. Microphysiological flux balance platform unravels the dynamics of drug induced steatosis. Lab Chip 2018, 18, 2510–2522.
  48. Perottoni, S.; Neto, N.G.B.; Di Nitto, C.; Dmitriev, R.I.; Raimondi, M.T.; Monaghan, M.G. Intracellular label-free detection of mesenchymal stem cell metabolism within a perivascular niche-on-a-chip. Lab Chip 2021, 21, 1395–1408.
  49. Horka, M.; Sun, S.; Ruszczak, A.; Garstecki, P.; Mayr, T. Lifetime of Phosphorescence from Nanoparticles Yields Accurate Measurement of Concentration of Oxygen in Microdroplets, Allowing One To Monitor the Metabolism of Bacteria. Anal. Chem. 2016, 88, 12006–12012.
  50. Ehgartner, J.; Strobl, M.; Bolivar, J.M.; Rabl, D.; Rothbauer, M.; Ertl, P.; Borisov, S.M.; Mayr, T. Simultaneous Determination of Oxygen and pH Inside Microfluidic Devices Using Core-Shell Nanosensors. Anal. Chem. 2016, 88, 9796–9804.
  51. Lasave, L.C.; Borisov, S.M.; Ehgartner, J.; Mayr, T. Quick and simple integration of optical oxygen sensors into glass-based microfluidic devices. RSC Adv. 2015, 5, 70808–70816.
  52. Wu, C.-C.; Yasukawa, T.; Shiku, H.; Matsue, T. Fabrication of miniature Clark oxygen sensor integrated with microstructure. Sens. Actuators B Chem. 2005, 110, 342–349.
  53. Luo, J.; Dziubla, T.; Eitel, R. A low temperature co-fired ceramic based microfluidic Clark-type oxygen sensor for real-time oxygen sensing. Sens. Actuators B Chem. 2017, 240, 392–397.
  54. Liebisch, F.; Weltin, A.; Marzioch, J.; Urban, G.A.; Kieninger, J. Zero-consumption Clark-type microsensor for oxygen monitoring in cell culture and organ-on-chip systems. Sens. Actuators B Chem. 2020, 322, 128652.
  55. Moya, A.; Illa, X.; Gimenez, I.; Lazo-Fernandez, Y.; Villa, R.; Errachid, A.; Gabriel, G. Miniaturized multiparametric flexible platform for the simultaneous monitoring of ionic: Application in real urine. Sens. Actuators B Chem. 2018, 255, 2861–2870.
  56. Bonk, S.M.; Stubbe, M.; Buehler, S.M.; Tautorat, C.; Baumann, W.; Klinkenberg, E.D.; Gimsa, J. Design and Characterization of a Sensorized Microfluidic Cell-Culture System with Electro-Thermal Micro-Pumps and Sensors for Cell Adhesion, Oxygen, and pH on a Glass Chip. Biosensors 2015, 5, 513–536.
  57. Weltin, A.; Slotwinski, K.; Kieninger, J.; Moser, I.; Jobst, G.; Wego, M.; Ehret, R.; Urban, G.A. Cell culture monitoring for drug screening and cancer research: A transparent, microfluidic, multi-sensor microsystem. Lab Chip 2014, 14, 138–146.
  58. Pereira Rodrigues, N.; Sakai, Y.; Fujii, T. Cell-based microfluidic biochip for the electrochemical real-time monitoring of glucose and oxygen. Sens. Actuators B Chem. 2008, 132, 608–613.
  59. Li, C.; Wu, P.-M.; Jung, W.; Ahn, C.H.; Shutter, L.A.; Narayan, R.K. A novel lab-on-a-tube for multimodality neuromonitoring of patients with traumatic brain injury (TBI). Lab Chip 2009, 9, 1988–1990.
  60. Sekli Belaïdi, F.; Salvagnac, L.; Assié Souleille, S.; Blatché, M.C.; Bedel-Pereira, E.; Séguy, I.; Temple-Boyer, P.; Launay, J. Accurate physiological monitoring using lab-on-a-chip platform for aquatic micro-organisms growth and optimized culture. Sens. Actuators B Chem. 2020, 321, 128492.
  61. Van Rossem, F.; Bomer, J.G.; de Boer, H.L.; Abbas, Y.; de Weerd, E.; van den Berg, A.; Le Gac, S. Sensing oxygen at the millisecond time-scale using an ultra-microelectrode array (UMEA). Sens. Actuators B Chem. 2017, 238, 1008–1016.
  62. Vonau, W.; Gerlach, F.; Herrmann, S. Conception of a new technique in cell cultivation using a lab-on-chip aided miniaturised device with calibratable electrochemical sensors. Microchim. Acta 2010, 171, 451–456.
  63. Moya, A.; Ortega-Ribera, M.; Guimerà, X.; Sowade, E.; Zea, M.; Illa, X.; Ramon, E.; Villa, R.; Gracia-Sancho, J.; Gabriel, G. Online oxygen monitoring using integrated inkjet-printed sensors in a liver-on-a-chip system. Lab Chip 2018, 18, 2023–2035.
  64. Weltin, A.; Hammer, S.; Noor, F.; Kaminski, Y.; Kieninger, J.; Urban, G.A. Accessing 3D microtissue metabolism: Lactate and oxygen monitoring in hepatocyte spheroids. Biosens. Bioelectron. 2017, 87, 941–948.
  65. Alexander, F., Jr.; Eggert, S.; Wiest, J. A novel lab-on-a-chip platform for spheroid metabolism monitoring. Cytotechnology 2018, 70, 375–386.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback