Diamond Electrodes for Neurochemical Sensing: Comparison
Please note this is a comparison between Version 3 by Bruce Ren and Version 2 by Bruce Ren.

Carbon-based electrodes combined with fast-scan cyclic voltammetry (FSCV) enable neurochemical sensing with high spatiotemporal resolution and sensitivity. While their attractive electrochemical and conductive properties have established a long history of use in the detection of neurotransmitters both in vitro and in vivo, carbon fiber microelectrodes (CFMEs) also have limitations in their fabrication, flexibility, and chronic stability. Diamond is a form of carbon with a more rigid bonding structure (sp3-hybridized) which can become conductive when boron-doped. Boron-doped diamond (BDD) is characterized by an extremely wide potential window, low background current, and good biocompatibility. Additionally, methods for processing and patterning diamond allow for high-throughput batch fabrication and customization of electrode arrays with unique architectures.

  • diamond
  • neurotransmitter
  • FSCV
  • electrode
  • sensing

1. Introduction to Carbon-Based Sensors for Neurochemical Sensing

Disruption of chemical or electrical signaling in the brain underlies neurological disorders such as addiction [1][2][3], Alzheimer’s disease [4][5][6], amyotrophic lateral sclerosis [7][8][9][10][11], chronic pain [12][13][14][15], depression [16][17][18], Huntington’s disease [19][20][21], Parkinson’s disease [22][23][24], and schizophrenia [25][26][27]. Detection methods for sensing neurochemicals in vivo for the study of neurological disorders would ideally be simultaneously sensitive, minimally-invasive, chronically stable, and relatively inexpensive. In a recent review by S. Niyonambaza et al., techniques for neurotransmitter (NT) identification and quantification were discussed in depth [28], including positron emission tomography and single photon NT identification and measurement [29][30][31], single-photon emission computed tomography [32][33], surface-enhanced Raman spectroscopy [34][35], fast-scan cyclic voltammetry (FSCV) [36][37][38], amperometry [39][40], high performance liquid column chromatography (HPLC) [41][42][43], fluorescence [44][45], optical fiber sensing [46][47], and colorimetric measurements [48][49][50], as seen in Table 1. Longitudinal positron emission tomography (PET), while non-invasive, is not adequately sensitive to detect subtle changes in dopamine (DA) levels. While each available technique yields useful information, it is fast-scan cyclic voltammetry (FSCV) and HPLC coupled with microdialysis that allow for high temporal and spatial resolution for the detection of neurotransmitters (NTs) [51][52][53][54]. Of these two techniques, HPLC-coupled microdialysis can have a temporal resolution of up to 1 min by combining injection and analysis, separating and quantifying various NTs. Microdialysis is a powerful technique due to its sensitivity, selectivity, and number of simultaneous metabolites that can be separated and quantified [55]. Classically, however, microdialysis lacks spatiotemporal resolution because it has a relatively large probe diameter (~200 μm) and a typical sample collection time of every ~5–20 min [55][56]. Likewise, microdialysis probes are associated with pronounced glial encapsulation and disruption of blood vessels in comparison to small-diameter carbon fiber microelectrodes (CFMEs) traditionally used for FSCV [57]. CFMEs detected a ~90% decrease in DA concentration within the immediate vicinity of a microdialysis probe (~200 microns) in comparison to levels measured ~1 mm away following probe insertion [58]. The relatively large scale of the microdialysis probe may disrupt release and reuptake of the neurochemicals of interest. These results imply that the accuracy of NT detection may be influenced by the tissue damage caused by microdialysis probes, motivating the development of improved technology.

As an alternative, FSCV has been used to measure sub-second neurochemical signaling through electrochemical detection in situ [59]. FSCV has been widely used for real-time detection of NTs and other important bioanalytes, including oxygen (O2) and pH change. It provides improved spatiotemporal resolution compared to other short-time scale electroanalytical techniques such as chronoamperometry (CA). The technique uses an ultramicroelectrode with a small biological footprint (~7 μm in diameter) to produce a background-subtracted signal with high temporal resolution and nanomolar sensitivity [60][61][62][63][64][65][66]. FSCV involves two central steps: (1) adsorption of electroactive species of interest (e.g., neurochemicals) to the electrode surface is favored by the application of a small DC holding potential, and (2) a triangular voltage pulse is repeatedly swept across the interface to produce signature peaks in Faradaic current which result from oxidation/reduction of adsorbed neurochemicals. These peaks can be used to identify the specific neurochemical (based on the corresponding potentials) as well as its concentration (based on current amplitude). The typical voltage waveform that has been optimized to achieve selectivity, sensitivity, and stability for measuring DA with CFMEs is an applied potential swept from −0.4 to 1.3 to −0.4 V at 400 V/s, and reapplied at a frequency of 10 Hz [67][68]. Using this waveform and other developed waveforms, FSCV has been used to probe not only DA and serotonin (5-HT), but also other oxidizable neurochemicals, such as 3,4-dihydroxyphenylacetic acid (DOPAC), purines, ascorbic acid (AA), adenosine, norepinephrine (NEP), oxygen, pH changes, and hydrogen peroxide in vivo . Improvements in selectivity and sensitivity have been achieved through further development of FSCV waveforms and application rate optimization. Development has also expanded NT measurement from phasic to tonic quantification and worked to increase technique safety and address biofouling effects on the chemical measurement [69][70][71][72][73][74].

2. CFME

The most commonly used materials for NT measurement with FSCV are carbon fibers due to their biocompatibility, electrochemical, and conductive properties [75][76][77][78]. CFMEs have been the cornerstone of in vivo FSCV and a bevy of data exist for the detection of electroactive NTs like DA, serotonin (5-HT), DOPAC, and others. Ralph Adam’s lab was the first to electrochemically measure DA in vivo using a carbon electrode consisting of graphite mixed with mineral oil [79]. Later, CFMEs were developed and used for dopaminergic and electrophysiological measurements in vivo, first published by Gonon [80], and then by Armstrong-James and Millar in 1979 [81]. Using the CFME, in 1981, Millar developed the technique of FSCV that was later popularized by Wightman [81][82][83]. A typical CFME consists of a carbon fiber that is aspirated into either a glass or silica capillary, or encased in some other insulating medium, such as parylene-C [84][85][86]. Electrodes also can be coated with polymers and other carbon-based materials to enhance the sensitivity and selectivity for various NTs [87][88][89]. Additionally, CFME surfaces can be functionalized with ease to tune the electrode to increase selectivity and decrease biofouling. Such coatings include: Poly(3,4-ethylenedioxythiophene) (PEDOT):Nafion, PEDOT:phosphorylcholrine [90], PEDOT: poly(ethyleneimine) (PEI) [88], CFME:gold nanoparticle [91], carbon nanospikes [92], Nafion carbon nanotubes [93][94], polycrystalline boron doped diamond [95][96], and carbon nanotube yarn [97][98]. Each coating has been tailored to not only increase sensitivity to NTs such as DA, but also to decrease the effects of biofouling and increase in vivo sensor lifetime.

Table 1. Summary of neurotransmitter detection techniques.[99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117][118][119]

Techniques

Advantages

Shortcomings

Reported LOD

PET

High spatial resolution

Complex manipulation

Very high cost

Dopamine: 200 nM [99]

SPECT

High spatial resolution

Complex manipulation

Very high cost

 

SERS

Very high sensitivity and

selectivity

Can be inapplicable in vivo depending on used material

Choline: 2 µM

Acetylcholine: 4 µM

Dopamine: 100 Nm

Epinephrine: 100 µM

FSCV

High sensitivity

Low selectivity

Electrode short lifetime

Dopamine: 50 nM

Amperometry

Low implementation cost

Low sensitivity and selectivity

Dopamine: 10 nM [100]

HPLC

High sensitivity and

selectivity

High cost and complex

manipulation

 

Fluorescence

High sensitivity and selectivity

May not be usable in vivo

Dopamine: 10 pM

Chemilumin-escence

High sensitivity, and ease to couple with other methods

Indirect measurement through the loss of a signal due to a binding event

6 nM

Optical Fiber Sensing

High selectivity

Low sensitivity

Glutamate: 0.22 µM

Colorimetric

High sensitivity and

selectivity, low cost

Not usable in vivo

Dopamine: 1.8 nM

Noradrenaline: 20 µM

Adrenaline: 2.5 µM

Table based on S. Niyonambaza et al. (reproduced from [28] under a Creative Commons Attribution 4.0 International License).

Despite their advantages, CFMEs have drawbacks which have motivated the search for alternative materials for in vivo FSCV. CFMEs are brittle and easily broken during insertion into the brain. Likewise, their long-term stability is compromised by dissolution of the carbon fiber electrode material that can result in significant degradation and loss of sensitivity over time. CFMEs are often fabricated through proprietary mechanisms, using low-throughput assembly methods, and are designed for industrial processes rather than electrochemical purposes [101][101]. Recently, boron doped diamond (BDD) deposition and growth processes were developed that enable the wafer patterning and growth of custom-deposited carbon electrodes [96,102–105][102][103][104][105]. Through these growth processes, BDD was grown on tungsten wires and carbon fiber surfaces. More recently, custom BDD microelectrodes (BDDMEs) encapsulated with polycrystalline diamond were developed [96,101,104,106,107][106][107]. BDD is an attractive material because it has a low background current, a wide potential window, and good biocompatibility [108–110][108][109][110]. Using BDD, Rusinek et al. showed that BDDMEs were suitable for neurochemical measurement using an all-diamond-electrode rather than the deposition of BDD onto another medium [103]. As carbon fibers are proprietarily fabricated, limiting the modification of the material for an optimized structure–function relationship, BDD electrodes are attractive due to the tunability of the carbon sp2 to sp3 ratio [111–114][111][112][113][114]. Increasing the sp2 character of the BDD increases the density of electronic states, and provides catalytic sites for redox reactions through adsorption sites. By using the BDD growth process, electrodes can be further tailored to enhance specific electrochemical properties. Such modifications include adjusting the structure–function relationship of the material to enhance conductivity, decrease capacitance, and increase chemical functionalization for selectivity and sensitivity. Additionally, recent advances in electrode array technologies for voltammetric measurements include multi-barrel glass capillary arrays [115[115][116],116], patterned arrays on silicon wafers [117[117][118],118], or parylene-C insulated multichannel carbon fiber electrode arrays [85,119][119]. While these arrays are powerful, most rely on hand fabrication processes under a microscope and are cumbersome, and slow. Open opportunities remain to improve the design and performance of carbon-based neurochemical sensors, including the development and optimization of diamond-based electrodes.

References

  1. Volkow, N.D.; Wang, G.J.; Fowler, J.S.; Tomasi, D.; Telang, F.; Baler, R. Addiction: Decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain’s control circuit. BioEssays 2010, 32, 748–755, doi:10.1002/bies.201000042.
  2. Sulzer, D. How Addictive Drugs Disrupt Presynaptic Dopamine Neurotransmission. Neuron 2011, 69, 628–649, doi:10.1016/j.neuron.2011.02.010.
  3. Volkow, N.D.; Wang, G.J.; Fowler, J.S.; Tomasi, D.; Telang, F. Addiction: Beyond dopamine reward circuitry. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 15037–15042, doi:10.1073/pnas.1010654108.
  4. Pan, X.; Kaminga, A.C.; Wen, S.W.; Wu, X.; Acheampong, K.; Liu, A. Dopamine and dopamine receptors in Alzheimer’s disease: A systematic review and network meta-analysis. Front. Aging Neurosci. 2019, 10, 1–14, doi:10.3389/fnagi.2019.00175.
  5. Martorana, A.; Koch, G. Is dopamine involved in Alzheimer’s disease? Front. Aging Neurosci. 2014, 6, 1–6, doi:10.3389/fnagi.2014.00252.
  6. Nobili, A.; Latagliata, E.C.; Viscomi, M.T.; Cavallucci, V.; Cutuli, D.; Giacovazzo, G.; Krashia, P.; Rizzo, F.R.; Marino, R.; Federici, M.; et al. Dopamine neuronal loss contributes to memory and reward dysfunction in a model of Alzheimer’s disease. Nat. Commun. 2017, 8, doi:10.1038/ncomms14727.
  7. Janssens, J.; Vermeiren, Y.; van Faassen, M.; van der Ley, C.; Kema, I.P.; De Deyn, P.P. Monoaminergic and Kynurenergic Characterization of Frontotemporal Dementia and Amyotrophic Lateral Sclerosis in Cerebrospinal Fluid and Serum. Neurochem. Res. 2020, 45, 1191–1201, doi:10.1007/s11064-020-03002-5.
  8. Borasio, G.D.; Linke, R.; Schwarz, J.; Schlamp, V.; Abel, A.; Mozley, P.D.; Tatsch, K. Dopaminergic deficit in amyotrophic lateral sclerosis assessed with [I-123] IPT single photon emission computed tomography. J. Neurol. Neurosurg. Psychiatry 1998, 65, 263–265, doi:10.1136/jnnp.65.2.263.
  9. Chen, X.; Wales, P.; Quinti, L.; Zuo, F.; Moniot, S.; Herisson, F.; Rauf, N.A.; Wang, H.; Silverman, R.B.; Ayata, C.; et al. The sirtuin-2 inhibitor AK7 is neuroprotective in models of parkinson’s disease but not amyotrophic lateral sclerosis and cerebral ischemia. PLoS One 2015, 10, 1–15, doi:10.1371/journal.pone.0116919.
  10. Kato, S.; Oda, M.; Tanabe, H. Diminution of dopaminergic neurons in the substantia nigra of sporadic amyotrophic lateral sclerosis. Neuropathol. Appl. Neurobiol. 1993, 19, 300–304, doi:10.1111/j.1365-2990.1993.tb00444.x.
  11. Vermeiren, Y.; Janssens, J.; Van Dam, D.; De Deyn, P.P. Serotonergic dysfunction in amyotrophic lateral sclerosis and Parkinson’s disease: Similar mechanisms, dissimilar outcomes. Front. Neurosci. 2018, 12, 1–9, doi:10.3389/fnins.2018.00185.
  12. Taylor, A.M.W.; Becker, S.; Schweinhardt, P.; Cahill, C. Mesolimbic dopamine signaling in acute and chronic pain: Implications for motivation, analgesia, and addiction. Pain 2016, 157, 1194–1198, doi:10.1097/j.pain.0000000000000494.
  13. Navratilova, E.; Atcherley, C.W.; Porreca, F. Brain Circuits Encoding Reward from Pain Relief. Trends Neurosci. 2015, 38, 741–750, doi:10.1016/j.tins.2015.09.003.
  14. Serafini, R.A.; Pryce, K.D.; Zachariou, V. The Mesolimbic Dopamine System in Chronic Pain and Associated Affective Comorbidities. Biol. Psychiatry 2020, 87, 64–73, doi:10.1016/j.biopsych.2019.10.018.
  15. Ledermann, K.; Martin-Sölch, C. Chronic Pain, Dopamine and Depression: Insights from Research on Fibromyalgia. In Chronic Pain-Physiopathology and Treatment; IntechOpen, 2018.
  16. Baskerville, T.A.; Douglas, A.J. Dopamine and oxytocin interactions underlying behaviors: Potential contributions to behavioral disorders. CNS Neurosci. Ther. 2010, 16, 92–123, doi:10.1111/j.1755-5949.2010.00154.x.
  17. Belujon, P.; Grace, A.A. Dopamine system dysregulation in major depressive disorders. Int. J. Neuropsychopharmacol. 2017, 20, 1036–1046, doi:10.1093/ijnp/pyx056.
  18. Groves, P.M.; Young, S.J.; Wilson, C.J. Self-inhibition by dopaminergic neurones: Disruption by (±)-α-methyl-p-tyrosine pretreatment or anterior diencephalic lesions. Neuropharmacology 1976, 15, 755–762, doi:10.1016/0028-3908(76)90004-6.
  19. Chen, J.Y.; Wang, E.A.; Cepeda, C.; Levine, M.S. Dopamine imbalance in Huntington’s disease: A mechanism for the lack of behavioral flexibility. Front. Neurosci. 2013, 7, 1–14, doi:10.3389/fnins.2013.00114.
  20. Bibb, J.A.; Yan, Z.; Svenningsson, P.; Snyder, G.L.; Pieribone, V.A.; Horiuchi, A.; Nairn, A.C.; Messer, A.; Greengard, P. Severe deficiencies in dopamine signaling in presymptomatic Huntington’s disease mice. Proc. Natl. Acad. Sci. U. S. A. 2000, 97, 6809–6814, doi:10.1073/pnas.120166397.
  21. Cepeda, C.; Murphy, K.P.S.; Parent, M.; Levine, M.S. The role of dopamine in huntington’s disease; 1st ed.; Elsevier B.V., 2014; Vol. 211; ISBN 9780444634252.
  22. Iarkov, A.; Barreto, G.E.; Grizzell, J.A.; Echeverria, V. Strategies for the Treatment of Parkinson’s Disease: Beyond Dopamine. Front. Aging Neurosci. 2020, 12, 1–20, doi:10.3389/fnagi.2020.00004.
  23. Sidhu, A.; Wersinger, C.; Vernier, P. α-Synuclein regulation of the dopaminergic transporter: A possible role in the pathogenesis of Parkinson’s disease. FEBS Lett. 2004, 565, 1–5, doi:10.1016/j.febslet.2004.03.063.
  24. Michel, P.P.; Hirsch, E.C.; Hunot, S. Understanding Dopaminergic Cell Death Pathways in Parkinson Disease. Neuron 2016, 90, 675–691, doi:10.1016/j.neuron.2016.03.038.
  25. McCutcheon, R.A.; Krystal, J.H.; Howes, O.D. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry 2020, 19, 15–33, doi:10.1002/wps.20693.
  26. Dahoun, T.; Trossbach, S. V; Brandon, N.J.; Korth, C.; Howes, O.D. The impact of Disrupted-in-Schizophrenia 1 (DISC1) on the dopaminergic system: A systematic review. Transl. Psychiatry 2017, 7, 1–15, doi:10.1038/tp.2016.282.
  27. Brisch, R.; Saniotis, A.; Wolf, R.; Bielau, H.; Bernstein, H.G.; Steiner, J.; Bogerts, B.; Braun, K.; Kumaratilake, J.; Henneberg, M.; et al. The role of dopamine in schizophrenia from a neurobiological and evolutionary perspective: Old fashioned, but still in vogue. Front. Psychiatry 2014, 5, 1–11, doi:10.3389/fpsyt.2014.00047.
  28. Niyonambaza, S.D.; Kumar, P.; Xing, P.; Mathault, J.; De Koninck, P.; Boisselier, E.; Boukadoum, M.; Miled, A. A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research. Appl. Sci. 2019, 9, 4719, doi:10.3390/app9214719.
  29. Weinstein, J.J.; Van De Giessen, E.; Rosengard, R.J.; Xu, X.; Ojeil, N.; Brucato, G.; Gil, R.B.; Kegeles, L.S.; Laruelle, M.; Slifstein, M.; et al. PET imaging of dopamine-D2 receptor internalization in schizophrenia. Mol. Psychiatry 2018, 23, 1506–1511, doi:10.1038/mp.2017.107.
  30. Chalon, S.; Vercouillie, J.; Payoux, P.; Deloye, J.B.; Malherbe, C.; Le Jeune, F.; Arlicot, N.; Salabert, A.S.; Guilloteau, D.; Emond, P.; et al. The story of the dopamine transporter PET tracer [18F]LBT-999: From conception to clinical use. Front. Med. 2019, 6, 1–5, doi:10.3389/fmed.2019.00090.
  31. Volkow, N.D.; Fowler, J.S.; Gatley, S.J.; Logan, J.; Wang, G.J.; Ding, Y.S.; Dewey, S. Pet evaluation of the dopamine system of the human brain. J. Nucl. Med. 1996, 37, 1242–1256.
  32. Laruelle, M.; Abi-Dargham, A.; van Dyck, C.H.; Rosenblatt, W.; Zea-Ponce, Y.; Zoghbi, S.S.; Baldwin, R.M.; Charney, D.S.; Hoffer, P.B.; Kung, H.F. SPECT imaging of striatal dopamine release after amphetamine challenge. J. Nucl. Med. 1995, 36, 1182–1190.
  33. Booth, T.C.; Nathan, M.; Waldman, A.D.; Quigley, A.M.; Schapira, A.H.; Buscombe, J. The role of functional dopamine-transporter SPECT imaging in parkinsonian syndromes, part 1. Am. J. Neuroradiol. 2015, 36, 229–235, doi:10.3174/ajnr.A3970.
  34. Blackie, E.J.; Le Ru, E.C.; Etchegoin, P.G. Single-molecule surface-enhanced raman spectroscopy of nonresonant molecules. J. Am. Chem. Soc. 2009, 131, 14466–14472, doi:10.1021/ja905319w.
  35. Hemal, A.K.; Menon, M.; Vattikuti, R.; Vattikuti, P. Laparoscopic urologic surgery: Can our patients benefit while we learn? Indian J. Urol. 2002, 18, 195–197.
  36. Baur, J.E.; Kristensen, E.W.; May, L.J.; Wiedemann, D.J.; Wightman, R.M. Fast-scan voltammetry of biogenic amines. Anal. Chem. 1988, 60, 1268–1272, doi:10.1021/ac00164a006.
  37. Jackson, B.P.; Dietz, S.M.; Wightman, R.M. Fast-Scan Cyclic Voltammetry of 5-Hydroxytryptamine. Anal. Chem. 1995, 67, 1115–1120, doi:10.1021/ac00102a015.
  38. Heien, M.L.A. V; Johnson, M.A.; Wightman, R.M. Resolving Neurotransmitters Detected by Fast-Scan Cyclic Voltammetry. Anal. Chem. 2004, 76, 5697–5704, doi:10.1021/ac0491509.
  39. Westerink, R.H.S. Exocytosis: Using amperometry to study presynaptic mechanisms of neurotoxicity. Neurotoxicology 2004, 25, 461–470, doi:10.1016/j.neuro.2003.10.006.
  40. Bruns, D. Detection of transmitter release with carbon fiber electrodes. Methods 2004, 33, 312–321, doi:10.1016/j.ymeth.2004.01.004.
  41. Zhao, X.E.; Suo, Y.R. Simultaneous determination of monoamine and amino acid neurotransmitters in rat endbrain tissues by pre-column derivatization with high-performance liquid chromatographic fluorescence detection and mass spectrometric identification. Talanta 2008, 76, 690–697, doi:10.1016/j.talanta.2008.04.032.
  42. Chatterjee, D.; Gerlai, R. High precision liquid chromatography analysis of dopaminergic and serotoninergic responses to acute alcohol exposure in zebrafish. Behav. Brain Res. 2009, 200, 208–213, doi:10.1016/j.bbr.2009.01.016.
  43. Watson, C.J.; Venton, B.J.; Kennedy, R.T. In vivo measurements of neurotransmitters by microdialysis sampling. Anal. Chem. 2006, 78, 1391–1399, doi:10.1021/ac0693722.
  44. De Benedetto, G.E.; Fico, D.; Pennetta, A.; Malitesta, C.; Nicolardi, G.; Lofrumento, D.D.; De Nuccio, F.; La Pesa, V. A rapid and simple method for the determination of 3,4-dihydroxyphenylacetic acid, norepinephrine, dopamine, and serotonin in mouse brain homogenate by HPLC with fluorimetric detection. J. Pharm. Biomed. Anal. 2014, 98, 266–270, doi:10.1016/j.jpba.2014.05.039.
  45. Quay, W.B. Circadian rhythm in rat pineal serotonin and its modifications by estrous cycle and photoperiod. Gen. Comp. Endocrinol. 1963, 3, 473–479, doi:10.1016/0016-6480(63)90079-0.
  46. Leung, A.; Shankar, P.M.; Mutharasan, R. A review of fiber-optic biosensors. Sensors Actuators B Chem. 2007, 125, 688–703, doi:10.1016/j.snb.2007.03.010.
  47. Nazempour, R.; Zhang, Q.; Fu, R.; Sheng, X. Biocompatible and Implantable Optical Fibers and Waveguides for Biomedicine. Materials (Basel). 2018, 11, 1283, doi:10.3390/ma11081283.
  48. Lin, Y.; Chen, C.; Wang, C.; Pu, F.; Ren, J.; Qu, X. Silver nanoprobe for sensitive and selective colorimetric detection of dopaminevia robust Ag–catechol interaction. Chem. Commun. 2011, 47, 1181–1183, doi:10.1039/C0CC03700A.
  49. Kong, B.; Zhu, A.; Luo, Y.; Tian, Y.; Yu, Y.; Shi, G. Sensitive and Selective Colorimetric Visualization of Cerebral Dopamine Based on Double Molecular Recognition. Angew. Chemie 2011, 123, 1877–1880, doi:10.1002/ange.201007071.
  50. Feng, J.-J.; Guo, H.; Li, Y.-F.; Wang, Y.-H.; Chen, W.-Y.; Wang, A.-J. Single Molecular Functionalized Gold Nanoparticles for Hydrogen-Bonding Recognition and Colorimetric Detection of Dopamine with High Sensitivity and Selectivity. ACS Appl. Mater. Interfaces 2013, 5, 1226–1231, doi:10.1021/am400402c.
  51. Schultz, K.N.; Kennedy, R.T. Time-Resolved Microdialysis for In Vivo Neurochemical Measurements and Other Applications. Annu. Rev. Anal. Chem. 2008, 1, 627–661, doi:10.1146/annurev.anchem.1.031207.113047.
  52. Gu, H.; Varner, E.L.; Groskreutz, S.R.; Michael, A.C.; Weber, S.G. In Vivo Monitoring of Dopamine by Microdialysis with 1 min Temporal Resolution Using Online Capillary Liquid Chromatography with Electrochemical Detection. Anal. Chem. 2015, 87, 6088–6094, doi:10.1021/acs.analchem.5b00633.
  53. Heien, M.L.A. V; Khan, A.S.; Ariansen, J.L.; Cheer, J.F.; Phillips, P.E.M.; Wassum, K.M.; Wightman, R.M. Real-time measurement of dopamine fluctuations after cocaine in the brain of behaving rats. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 10023–10028, doi:10.1073/pnas.0504657102.
  54. Beyene, A.G.; Yang, S.J.; Landry, M.P. Review Article: Tools and trends for probing brain neurochemistry. J. Vac. Sci. Technol. A 2019, 37, 40802, doi:10.1116/1.5051047.
  55. Rodeberg, N.T.; Sandberg, S.G.; Johnson, J.A.; Phillips, P.E.M.; Wightman, R.M. Hitchhiker’s Guide to Voltammetry: Acute and Chronic Electrodes for in Vivo Fast-Scan Cyclic Voltammetry. ACS Chem. Neurosci. 2017, 8, 221–234, doi:10.1021/acschemneuro.6b00393.
  56. Kennedy, R.T. Emerging trends in in vivo neurochemical monitoring by microdialysis. Curr. Opin. Chem. Biol. 2013, 17, 860–867, doi:10.1016/j.cbpa.2013.06.012.
  57. Jaquins-Gerstl, A.; Michael, A.C. Comparison of the brain penetration injury associated with microdialysis and voltammetry. J. Neurosci. Methods 2009, 183, 127–135, doi:10.1016/j.jneumeth.2009.06.023.
  58. Borland, L.M.; Shi, G.; Yang, H.; Michael, A.C. Voltammetric study of extracellular dopamine near microdialysis probes acutely implanted in the striatum of the anesthetized rat. J. Neurosci. Methods 2005, 146, 149–158, doi:10.1016/j.jneumeth.2005.02.002.
  59. Ou, Y.; Buchanan, A.M.; Witt, C.E.; Hashemi, P. Frontiers in electrochemical sensors for neurotransmitter detection: Towards measuring neurotransmitters as chemical diagnostics for brain disorders. Anal. Methods 2019, 11, 2738–2755, doi:10.1039/c9ay00055k.
  60. Puthongkham, P.; Venton, B.J. Recent advances in fast-scan cyclic voltammetry. Analyst 2020, 145, 1087–1102, doi:10.1039/c9an01925a.
  61. Adams, R. In vivo electrochemical measurements in the CNS. Prog. Neurobiol. 1990, 35, 297–311, doi:10.1016/0301-0082(90)90014-8.
  62. Keithley, R.B.; Takmakov, P.; Bucher, E.S.; Belle, A.M.; Owesson-White, C.A.; Park, J.; Wightman, R.M. Higher Sensitivity Dopamine Measurements with Faster-Scan Cyclic Voltammetry. Anal. Chem. 2011, 83, 3563–3571, doi:10.1021/ac200143v.
  63. Roberts, J.G.; Sombers, L.A. Fast-Scan Cyclic Voltammetry: Chemical Sensing in the Brain and beyond. Anal. Chem. 2018, 90, 490–504, doi:10.1021/acs.analchem.7b04732.
  64. Kasasbeh, A.; Lee, K.; Bieber, A.; Bennet, K.; Chang, S.-Y. Wireless Neurochemical Monitoring in Humans. Stereotact. Funct. Neurosurg. 2013, 91, 141–147, doi:10.1159/000345111.
  65. Wilson, L.R.; Panda, S.; Schmidt, A.C.; Sombers, L.A. Selective and Mechanically Robust Sensors for Electrochemical Measurements of Real-Time Hydrogen Peroxide Dynamics in Vivo. Anal. Chem. 2018, 90, 888–895, doi:10.1021/acs.analchem.7b03770.
  66. Takmakov, P.; Zachek, M.K.; Keithley, R.B.; Bucher, E.S.; McCarty, G.S.; Wightman, R.M. Characterization of local pH changes in brain using fast-scan cyclic voltammetry with carbon microelectrodes. Anal. Chem. 2010, 82, 9892–9900, doi:10.1021/ac102399n.
  67. Bath, B.D.; Michael, D.J.; Trafton, B.J.; Joseph, J.D.; Runnels, P.L.; Wightman, R.M. Subsecond adsorption and desorption of dopamine at carbon-fiber microelectrodes. Anal. Chem. 2000, 72, 5994–6002, doi:10.1021/ac000849y.
  68. Venton, B.J.; Cao, Q. Fundamentals of fast-scan cyclic voltammetry for dopamine detection. Analyst 2020, 145, 1158–1168, doi:10.1039/c9an01586h.
  69. Atcherley, C.W.; Laude, N.D.; Parent, K.L.; Heien, M.L. Fast-scan controlled-adsorption voltammetry for the quantification of absolute concentrations and adsorption dynamics. Langmuir 2013, 29, 14885–14892, doi:10.1021/la402686s.
  70. Atcherley, C.W.; Wood, K.M.; Parent, K.L.; Hashemi, P.; Heien, M.L. The coaction of tonic and phasic dopamine dynamics. Chem. Commun. (Camb). 2015, 51, 2235–2238, doi:10.1039/c4cc06165a.
  71. Oh, Y.; Heien, M.L.; Park, C.; Kang, Y.M.; Kim, J.; Boschen, S.L.; Shin, H.; Cho, H.U.; Blaha, C.D.; Bennet, K.E.; et al. Tracking tonic dopamine levels in vivo using multiple cyclic square wave voltammetry. Biosens. Bioelectron. 2018, 121, 174–182, doi:10.1016/j.bios.2018.08.034.
  72. Siegenthaler, J.R.; Gushiken, B.C.; Hill, D.F.; Cowen, S.L.; Heien, M.L. Moving Fast-Scan Cyclic Voltammetry toward FDA Compliance with Capacitive Decoupling Patient Protection. ACS Sensors 2020, acssensors.9b02249, doi:10.1021/acssensors.9b02249.
  73. Seaton, B.T.; Hill, D.F.; Cowen, S.L.; Heien, M.L. Mitigating the Effects of Electrode Biofouling-Induced Impedance for Improved Long-Term Electrochemical Measurements in Vivo. Anal. Chem. 2020, 92, 6334–6340, doi:10.1021/acs.analchem.9b05194.
  74. Vreeland, R.F.; Atcherley, C.W.; Russell, W.S.; Xie, J.Y.; Lu, D.; Laude, N.D.; Porreca, F.; Heien, M.L. Biocompatible PEDOT:Nafion composite electrode coatings for selective detection of neurotransmitters in vivo. Anal. Chem. 2015, 87, 2600–7, doi:10.1021/ac502165f.
  75. Trouillon, R.; Lin, Y.; Mellander, L.J.; Keighron, J.D.; Ewing, A.G. Evaluating the Diffusion Coefficient of Dopamine at the Cell Surface During Amperometric Detection: Disk vs Ring Microelectrodes. Anal. Chem. 2013, 85, 6421–6428, doi:10.1021/ac400965d.
  76. Song, H.; Reed, M.A.; Lee, T. Single molecule electronic devices. Adv. Mater. 2011, 23, 1583–1608, doi:10.1002/adma.201004291.
  77. Wightman, R.M. Probing Cellular Chemistry in Biological Systems with Microelectrodes. Science (80-. ). 2006, 311, 1570–1574, doi:10.1126/science.1120027.
  78. Deakin, M.R.; Wightman, R.M.; Amatore, C.A. Electrochemical kinetics at microelectrodes. Part II. Cyclic voltammetry at band electrodes. J. Electroanal. Chem. 1986, 215, 49–61, doi:10.1016/0022-0728(86)87004-8.
  79. Kissinger, P.T.; Hart, J.B.; Adams, R.N. Voltammetry in brain tissue - a new neurophysiological measurement. Brain Res. 1973, 55, 209–213, doi:10.1016/0006-8993(73)90503-9.
  80. Gonon, F.; Cespuglio, R.; Ponchon, J.L.; Buda, M.; Jouvet, M.; Adams, R.N.; Pujol, J.F. [In vivo continuous electrochemical determination of dopamine release in rat neostriatum]. C. R. Acad. Sci. Hebd. Seances Acad. Sci. D. 1978, 286, 1203–1206.
  81. Armstrong-James, M.; Millar, J. Carbon fibre microelectrodes. J. Neurosci. Methods 1979, 1, 279–287, doi:10.1016/0165-0270(79)90039-6.
  82. Millar, J.; Armstrong-James, M.; Kruk, Z.L. Polarographic assay of iontophoretically applied dopamine and low-noise unit recording using a multibarrel carbon fibre microelectrode. Brain Res. 1981, 205, 419–424, doi:10.1016/0006-8993(81)90354-1.
  83. Millar, J.; Stamford, J.A.; Kruk, Z.L.; Wightman, R.M. Electrochemical, pharmacological and electrophysiological evidence of rapid dopamine release and removal in the rat caudate nucleus following electrical stimulation of the median forebrain bundle. Eur. J. Pharmacol. 1985, 109, 341–348, doi:10.1016/0014-2999(85)90394-2.
  84. Kawagoe, K.T.; Zimmerman, J.B.; Wightman, R.M. Principles of voltammetry and microelectrode surface states. J. Neurosci. Methods 1993, 48, 225–240, doi:10.1016/0165-0270(93)90094-8.
  85. Patel, P.R.; Na, K.; Zhang, H.; Kozai, T.D.Y.; Kotov, N.A.; Yoon, E.; Chestek, C.A. Insertion of linear 8.4 μm diameter 16 channel carbon fiber electrode arrays for single unit recordings. J. Neural Eng. 2015, 12, 46009, doi:10.1088/1741-2560/12/4/046009.
  86. Clark, J.J.; Sandberg, S.G.; Wanat, M.J.; Gan, J.O.; Horne, E.A.; Hart, A.S.; Akers, C.A.; Parker, J.G.; Willuhn, I.; Martinez, V.; et al. Chronic microsensors for longitudinal, subsecond dopamine detection in behaving animals. Nat. Methods 2010, 7, 126–129, doi:10.1038/nmeth.1412.
  87. Elgrishi, N.; Rountree, K.J.; McCarthy, B.D.; Rountree, E.S.; Eisenhart, T.T.; Dempsey, J.L. A Practical Beginner’s Guide to Cyclic Voltammetry. J. Chem. Educ. 2018, 95, 197–206, doi:10.1021/acs.jchemed.7b00361.
  88. Raju, D.; Mendoza, A.; Wonnenberg, P.; Mohanaraj, S.; Sarbanes, M.; Truong, C.; Zestos, A.G. Polymer modified carbon fiber-microelectrodes and waveform modifications enhance neurotransmitter metabolite detection. Anal. Methods 2019, 11, 1620–1630, doi:10.1039/C8AY02737D.
  89. Zestos, A.G.; Yang, C.; Jacobs, C.B.; Hensley, D.; Venton, B.J. Carbon nanospikes grown on metal wires as microelectrode sensors for dopamine. Analyst 2015, 140, 7283–7292, doi:10.1039/C5AN01467K.
  90. Liu, X.; Xiao, T.; Wu, F.; Shen, M.Y.; Zhang, M.; Yu, H.H.; Mao, L. Ultrathin Cell-Membrane-Mimic Phosphorylcholine Polymer Film Coating Enables Large Improvements for In Vivo Electrochemical Detection. Angew. Chemie - Int. Ed. 2017, 56, 11802–11806, doi:10.1002/anie.201705900.
  91. Mohanaraj, S.; Wonnenberg, P.; Cohen, B.; Zhao, H.; Hartings, M.R.; Zou, S.; Fox, D.M.; Zestos, A.G. Gold nanoparticle modified carbon fiber microelectrodes for enhanced neurochemical detection. J. Vis. Exp. 2019, 2019, 1–9, doi:10.3791/59552.
  92. Cao, Q.; Hensley, D.K.; Lavrik, N. V; Venton, B.J. Carbon nanospikes have better electrochemical properties than carbon nanotubes due to greater surface roughness and defect sites. Carbon N. Y. 2019, 155, 250–257, doi:10.1016/j.carbon.2019.08.064.
  93. Peairs, M.J.; Ross, A.E.; Venton, B.J. Comparison of Nafion- and overoxidized polypyrrole-carbon nanotube electrodes for neurotransmitter detection. Anal. Methods 2011, 3, 2379–2386, doi:10.1039/c1ay05348e.
  94. Ross, A.E.; Venton, B.J. Nafion–CNT coated carbon-fiber microelectrodes for enhanced detection of adenosine. Analyst 2012, 137, 3045, doi:10.1039/c2an35297d.
  95. Bennet, K.E.; Lee, K.H.; Kruchowski, J.N.; Chang, S.Y.; Marsh, M.P.; Van Orsow, A.A.; Paez, A.; Manciu, F.S. Development of Conductive Boron-Doped Diamond Electrode: A microscopic, Spectroscopic, and Voltammetric Study. Materials (Basel). 2013, 6, 5726–5741, doi:10.3390/ma6125726.
  96. Bennet, K.E.; Tomshine, J.R.; Min, H.K.; Manciu, F.S.; Marsh, M.P.; Paek, S.B.; Settell, M.L.; Nicolai, E.N.; Blaha, C.D.; Kouzani, A.Z.; et al. A diamond-based electrode for detection of neurochemicals in the human brain. Front. Hum. Neurosci. 2016, 10, 1–12, doi:10.3389/fnhum.2016.00102.
  97. Schmidt, A.C.; Wang, X.; Zhu, Y.; Sombers, L.A. Carbon Nanotube Yarn Electrodes for Enhanced Detection of Neurotransmitter Dynamics in Live Brain Tissue. ACS Nano 2013, 7, 7864–7873, doi:10.1021/nn402857u.
  98. Yang, C.; Trikantzopoulos, E.; Jacobs, C.B.; Venton, B.J. Evaluation of carbon nanotube fiber microelectrodes for neurotransmitter detection: Correlation of electrochemical performance and surface properties. Anal. Chim. Acta 2017, 965, 1–8, doi:10.1016/j.aca.2017.01.039.
  99. Ceccarini, J.; Vrieze, E.; Koole, M.; Muylle, T.; Bormans, G.; Claes, S.; Van Laere, K. Optimized In Vivo Detection of Dopamine Release Using 18F-Fallypride PET. J. Nucl. Med. 2012, 53, 1565–1572, doi:10.2967/jnumed.111.099416.
  100. Njagi, J.; Chernov, M.M.; Leiter, J.C.; Andreescu, S. Amperometric Detection of Dopamine in Vivo with an Enzyme Based Carbon Fiber Microbiosensor. Anal. Chem. 2010, 82, 989–996, doi:10.1021/ac9022605.
  101. Manciu, F.S.; Oh, Y.; Barath, A.; Rusheen, A.E.; Kouzani, A.Z.; Hodges, D.; Guerrero, J.; Tomshine, J.; Lee, K.H.; Bennet, K.E. Analysis of carbon-based microelectrodes for neurochemical sensing. Materials (Basel). 2019, 12, doi:10.3390/ma12193186.
  102. Adams, G.L.; Carroll, P.J.; Smith, A.B. Total Synthesis of (+)-Scholarisine A. J. Am. Chem. Soc. 2012, 134, 4037–4040, doi:10.1021/ja211840k.
  103. Rusinek, C.A.; Guo, Y.; Rechenberg, R.; Becker, M.F.; Purcell, E.; Verber, M.; McKinney, C.; Li, W. All-Diamond Microfiber Electrodes for Neurochemical Analysis. J. Electrochem. Soc. 2018, 165, G3087--G3092, doi:10.1149/2.0141812jes.
  104. Puthongkham, P.; Venton, B.J. Nanodiamond Coating Improves the Sensitivity and Antifouling Properties of Carbon Fiber Microelectrodes. ACS Sensors 2019, 4, 2403–2411, doi:10.1021/acssensors.9b00994.
  105. Suzuki, A.; Ivandini, T.A.; Yoshimi, K.; Fujishima, A.; Oyama, G.; Nakazato, T.; Hattori, N.; Kitazawa, S.; Einaga, Y. Fabrication, Characterization, and Application of Boron-Doped Diamond Microelectrodes for in Vivo Dopamine Detection. Anal. Chem. 2007, 79, 8608–8615, doi:10.1021/ac071519h.
  106. Siddiqui, S.; Dutta, G.; Tan, C.; Arumugam, P.U. Nanocrystalline Diamond Electrodes: Enabling electrochemical microsensing applications with high reliability and stability. IEEE Nanotechnol. Mag. 2016, 10, 12–20, doi:10.1109/MNANO.2016.2572243.
  107. Trouillon, R.; Einaga, Y.; Gijs, M.A.M. Cathodic pretreatment improves the resistance of boron-doped diamond electrodes to dopamine fouling. Electrochem. commun. 2014, 47, 92–95, doi:10.1016/j.elecom.2014.07.028.
  108. Alcaide, M.; Taylor, A.; Fjorback, M.; Zachar, V.; Pennisi, C.P. Boron-doped nanocrystalline diamond electrodes for neural interfaces: In vivo biocompatibility evaluation. Front. Neurosci. 2016, 10, 1–9, doi:10.3389/fnins.2016.00087.
  109. Grill, A. Diamond-like carbon coatings as biocompatible materials - An overview. Diam. Relat. Mater. 2003, 12, 166–170, doi:10.1016/S0925-9635(03)00018-9.
  110. Roeser, J.; Alting, N.F.A.; Permentier, H.P.; Bruins, A.P.; Bischoff, R. Boron-doped diamond electrodes for the electrochemical oxidation and cleavage of peptides. Anal. Chem. 2013, 85, 6626–6632, doi:10.1021/ac303795c.
  111. Kissinger, Peter; Heineman, W.R. Laboratory Techniques in Electroanalytical Chemistry, Second Edition, Revised and Expanded - CRC Press Book. In CRC Press; 1996 ISBN 0824794451.
  112. Muzyka, K.; Sun, J.; Fereja, T.H.; Lan, Y.; Zhang, W.; Xu, G. Boron-doped diamond: Current progress and challenges in view of electroanalytical applications. Anal. Methods 2019, 11, 397–414, doi:10.1039/c8ay02197j.
  113. Bennett, J.A.; Wang, J.; Show, Y.; Swain, G.M. Effect of sp[sup 2]-Bonded Nondiamond Carbon Impurity on the Response of Boron-Doped Polycrystalline Diamond Thin-Film Electrodes. J. Electrochem. Soc. 2004, 151, E306, doi:10.1149/1.1780111.
  114. Wang, S.; Swain, G.M. Spatially heterogeneous electrical and electrochemical properties of hydrogen-terminated boron-doped nanocrystalline diamond thin film deposited from an argon-rich CH4/H2/Ar/B2H6 source gas mixture. J. Phys. Chem. C 2007, 111, 3986–3995, doi:10.1021/jp0669557.
  115. Zhang, B.; Heien, M.L.A. V; Santillo, M.F.; Mellander, L.; Ewing, A.G. Temporal resolution in electrochemical imaging on single PC12 cells using amperometry and voltammetry at microelectrode arrays. Anal. Chem. 2011, 83, 571–577, doi:10.1021/ac102502g.
  116. Zhang, B.; Adams, K.L.; Luber, S.J.; Eves, D.J.; Heien, M.L.; Ewing, A.G. Spatially and temporally resolved single-cell exocytosis utilizing individually addressable carbon microelectrode arrays. Anal. Chem. 2008, 80, 1394–1400, doi:10.1021/ac702409s.
  117. Wigström, J.; Dunevall, J.; Najafinobar, N.; Lovrić, J.; Wang, J.; Ewing, A.G.; Cans, A.S. Lithographic Microfabrication of a 16-Electrode Array on a Probe Tip for High Spatial Resolution Electrochemical Localization of Exocytosis. Anal. Chem. 2016, 88, 2080–2087, doi:10.1021/acs.analchem.5b03316.
  118. Zachek, M.K.; Park, J.; Takmakov, P.; Wightman, R.M.; McCarty, G.S. Microfabricated FSCV-compatible microelectrode array for real-time monitoring of heterogeneous dopamine release. Analyst 2010, 135, 1556–1563, doi:10.1039/c0an00114g.
  119. Schwerdt, H.N.; Kim, M.J.; Amemori, S.; Homma, D.; Yoshida, T.; Shimazu, H.; Yerramreddy, H.; Karasan, E.; Langer, R.; Graybiel, A.M.; et al. Subcellular probes for neurochemical recording from multiple brain sites. Lab Chip 2017, 17, 1104–1115, doi:10.1039/c6lc01398h.
More
Video Production Service