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Annual Review of Chemical and Biomolecular Engineering

Volume 12, 2021

Infochemistry and the Future of Chemical Information Processing

Abstract

Nowadays, information processing is based on semiconductor (e.g., silicon) devices. Unfortunately, the performance of such devices has natural limitations owing to the physics of semiconductors. Therefore, the problem of finding new strategies for storing and processing an ever-increasing amount of diverse data is very urgent. To solve this problem, scientists have found inspiration in nature, because living organisms have developed uniquely productive and efficient mechanisms for processing and storing information. We address several biological aspects of information and artificial models mimicking corresponding bioprocesses. For instance, we review the formation of synchronization patterns and the emergence of order out of chaos in model chemical systems. We also consider molecular logic and ion fluxes as information carriers. Finally, we consider recent progress in infochemistry, a new direction at the interface of chemistry, biology, and computer science, considering unconventional methods of information processing.

Keyword(s): infochemistry, iontronics, molecular logic gates, nonlinear oscillators, self-assembly, sensors
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/content/journals/10.1146/annurev-chembioeng-122120-023514
2021-06-07
2024-04-16
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Literature Cited

  1. 1. 
    Brinkman W, Haggan D, Troutman W. 1997. A history of the invention of the transistor and where it will lead us. IEEE J. Solid-State Circ. 32:121858–65
     [Google Scholar]
  2. 2. 
    Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:6245255–60
     [Google Scholar]
  3. 3. 
    Li W, Lu W, Fuh J, Wong Y. 2005. Collaborative computer-aided design—research and development status. Comput.-Aided Des 37:9931–40
     [Google Scholar]
  4. 4. 
    Venema L. 2011. Silicon electronics and beyond. Nature 479:7373309
     [Google Scholar]
  5. 5. 
    Keyes RW. 2005. Physical limits of silicon transistors and circuits. Rep. Progress Phys. 68:122701–46
     [Google Scholar]
  6. 6. 
    Tkačik G, Bialek W. 2016. Information processing in living systems. Annu. Rev. Condens. Matter Phys. 7:89–117
     [Google Scholar]
  7. 7. 
    Sweller J, Sweller S. 2006. Natural information processing systems. Evol. Psychol. 4:1). https://doi.org/10.1177/147470490600400135
     [Google Scholar]
  8. 8. 
    Panda D, Molla KA, Baig MJ, Swain A, Behera D, Dash M. 2018. DNA as a digital information storage device: Hope or hype? 3 Biotech 8:239
     [Google Scholar]
  9. 9. 
    Extance A. 2016. How DNA could store all the world's data. Nature 537:761822–24
     [Google Scholar]
  10. 10. 
    Scholz A, Reid G, Vogel W, Bostock H. 1993. Ion channels in human axons. J. Neurophysiol. 70:31274–79
     [Google Scholar]
  11. 11. 
    Nasrabadi NM. 2007. Pattern recognition and machine learning. J. Electron. Imaging 16:4049901
     [Google Scholar]
  12. 12. 
    Burstedde C, Klauck K, Schadschneider A, Zittartz J. 2001. Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys. A 295:3–4507–25
     [Google Scholar]
  13. 13. 
    Abraham A, Das S, Roy S 2008. Swarm intelligence algorithms for data clustering. Soft Computing for Knowledge Discovery and Data Mining O Maimon, L Rokach 279–313 New York: Springer Sci. Bus.
     [Google Scholar]
  14. 14. 
    Gu J, Wang Z, Kuen J, Ma L, Shahroudy A et al. 2018. Recent advances in convolutional neural networks. Pattern Recognit 77:354–77
     [Google Scholar]
  15. 15. 
    Handl J, Knowles J. 2007. An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11:156–76
     [Google Scholar]
  16. 16. 
    Ryzhkov NV, Andreeva DV, Skorb EV. 2019. Coupling pH-regulated multilayers with inorganic surfaces for bionic devices and infochemistry. Langmuir 35:8543–56
     [Google Scholar]
  17. 17. 
    Szaciłowski K. 2012. Infochemistry: Information Processing at the Nanoscale. Chichester, UK: John Wiley & Sons
  18. 18. 
    Thomas SW, Chiechi RC, LaFratta CN, Webb MR, Lee A et al. 2009. Infochemistry and infofuses for the chemical storage and transmission of coded information. PNAS 106239147–50
     [Google Scholar]
  19. 19. 
    Kim C, Thomas SW, Whitesides GM. 2010. Long-duration transmission of information with infofuses. Angew. Chem. Int. Ed. 49:274571–75
     [Google Scholar]
  20. 20. 
    Erbas-Cakmak S, Kolemen S, Sedgwick AC, Gunnlaugsson T, James TD et al. 2018. Molecular logic gates: the past, present and future. Chem. Soc. Rev. 47:72228–48
     [Google Scholar]
  21. 21. 
    Madhuprasad, Bhat MP, Jung H-Y, Losic D, Kurkuri MD 2016. Anion sensors as logic gates: A close encounter?. Chemistry 22:186148–78
     [Google Scholar]
  22. 22. 
    Zaszczynska A, Sajkiewicz P, Gradys A. 2020. Piezoelectric scaffolds as smart materials for neural tissue engineering. Polymers 12:1161
     [Google Scholar]
  23. 23. 
    Li J-F, Liu W-S, Zhao L-D, Zhou M. 2010. High-performance nanostructured thermoelectric materials. NPG Asia Mater 2:4152–58
     [Google Scholar]
  24. 24. 
    Polman A, Knight M, Garnett EC, Ehrler B, Sinke WC. 2016. Photovoltaic materials: present efficiencies and future challenges. Science 352:6283aad4424
     [Google Scholar]
  25. 25. 
    Shariat BS, Meng Q, Mahmud AS, Wu Z, Bakhtiari R et al. 2017. Functionally graded shape memory alloys: design, fabrication and experimental evaluation. Mater. Des. 124:225–37
     [Google Scholar]
  26. 26. 
    Li M-H, Keller P. 2009. Stimuli-responsive polymer vesicles. Soft Matter 5:5927–37
     [Google Scholar]
  27. 27. 
    Wu Y, Liu S, Tao Y, Ma C, Zhang Y et al. 2014. New strategy for controlled release of drugs. Potential pinpoint targeting with multiresponsive tetraaniline diblock polymer vesicles: site-directed burst release with voltage. ACS Appl. Mater. Interfaces 6:31470–80
     [Google Scholar]
  28. 28. 
    Chen T, Ferris R, Zhang J, Ducker R, Zauscher S. 2010. Stimulus-responsive polymer brushes on surfaces: transduction mechanisms and applications. Prog. Polym. Sci. 35:1–294–112
     [Google Scholar]
  29. 29. 
    Peng S, Bhushan B. 2012. Smart polymer brushes and their emerging applications. RSC Adv 2:238557–78
     [Google Scholar]
  30. 30. 
    Ballauff M, Lu Y. 2007.. “ Smart” nanoparticles: preparation, characterization and applications. Polymer 48:71815–23
     [Google Scholar]
  31. 31. 
    Moffitt MG. 2013. Self-assembly of polymer brush-functionalized inorganic nanoparticles: from hairy balls to smart molecular mimics. J. Phys. Chem. Lett. 4:213654–66
     [Google Scholar]
  32. 32. 
    Xia L-W, Xie R, Ju X-J, Wang W, Chen Q, Chu L-Y. 2013. Nano-structured smart hydrogels with rapid response and high elasticity. Nat. Commun. 4:2226
     [Google Scholar]
  33. 33. 
    Zhang F, Xiong L, Ai Y, Liang Z, Liang Q. 2018. Stretchable multiresponsive hydrogel with actuatable, shape memory, and self-healing properties. Adv. Sci. 5:81800450
     [Google Scholar]
  34. 34. 
    Motornov M, Roiter Y, Tokarev I, Minko S. 2010. Stimuli-responsive nanoparticles, nanogels and capsules for integrated multifunctional intelligent systems. Prog. Polym. Sci. 35:1–2174–211
     [Google Scholar]
  35. 35. 
    Wohl BM, Engbersen JF. 2012. Responsive layer-by-layer materials for drug delivery. J. Control. Release 158:12–14
     [Google Scholar]
  36. 36. 
    Silva APD, McClenaghan ND. 2004. Molecular-scale logic gates. Chemistry 10:3574–86
     [Google Scholar]
  37. 37. 
    Leung KC-F, Chak C-P, Lo C-M, Wong W-Y, Xuan S, Cheng CHK 2009. Ph-controllable supramolecular systems. Chemistry 4:3364–81
     [Google Scholar]
  38. 38. 
    Zhang Q, Ko NR, Oh JK. 2012. Recent advances in stimuli-responsive degradable block copolymer micelles: synthesis and controlled drug delivery applications. Chem. Commun. 48:617542–52
     [Google Scholar]
  39. 39. 
    Li Y, Rodrigues J, Tomás H. 2012. Injectable and biodegradable hydrogels: gelation, biodegradation and biomedical applications. Chem. Soc. Rev. 41:62193–221
     [Google Scholar]
  40. 40. 
    Garnier T, Dochter A, Chau NTT, Schaaf P, Jierry L, Boulmedais F. 2015. Surface confined self-assembly of polyampholytes generated from charge-shifting polymers. Chem. Commun. 51:7414092–95
     [Google Scholar]
  41. 41. 
    Zhang Q, Weber C, Schubert US, Hoogenboom R. 2017. Thermoresponsive polymers with lower critical solution temperature: from fundamental aspects and measuring techniques to recommended turbidimetry conditions. Mater. Horiz. 4:2109–16
     [Google Scholar]
  42. 42. 
    Chhabra A, Kanapuram RR, Kim TJ, Geng J, Silva AKD et al. 2013. Humidity effects on the wetting characteristics of poly(N-isopropylacrylamide) during a lower critical solution transition. Langmuir 29:258116–24
     [Google Scholar]
  43. 43. 
    Collier TO, Anderson JM, Kikuchi A, Okano T. 2001. Adhesion behavior of monocytes, macrophages, and foreign body giant cells on poly (N-isopropylacrylamide) temperature-responsive surfaces. J. Biomed. Mater. Res. 59:1136–43
     [Google Scholar]
  44. 44. 
    Beharry AA, Woolley GA. 2011. Azobenzene photoswitches for biomolecules. Chem. Soc. Rev. 40:84422–37
     [Google Scholar]
  45. 45. 
    Sadovski O, Beharry AA, Zhang F, Woolley GA. 2009. Spectral tuning of azobenzene photoswitches for biological applications. Angew. Chem. Int. Ed. 48:81484–86
     [Google Scholar]
  46. 46. 
    Wang C, Hashimoto K, Tamate R, Kokubo H, Watanabe M. 2017. Controlled sol-gel transitions of a thermoresponsive polymer in a photoswitchable azobenzene ionic liquid as a molecular trigger. Angew. Chem. Int. Ed. 57:1227–30
     [Google Scholar]
  47. 47. 
    Chen M, Yao B, Kappl M, Liu S, Yuan J et al. 2019. Entangled azobenzene-containing polymers with photoinduced reversible solid-to-liquid transitions for healable and reprocessable photoactuators. Adv. Funct. Mater. 30:41906752
     [Google Scholar]
  48. 48. 
    Ryzhkov NV, Skorb EV. 2020. A platform for light-controlled formation of free-stranding lipid membranes. J. R. Soc. Interface 17:20190740
     [Google Scholar]
  49. 49. 
    Maltanava HM, Poznyak SK, Andreeva DV, Quevedo MC, Bastos AC et al. 2017. Light-induced proton pumping with a semiconductor: vision for photoproton lateral separation and robust manipulation. ACS Appl. Mater. Interfaces 9:2824282–89
     [Google Scholar]
  50. 50. 
    Poghossian A, Katz E, Schöning MJ. 2015. Enzyme logic AND-Reset and OR-Reset gates based on a field-effect electronic transducer modified with multi-enzyme membrane. Chem. Commun. 51:306564–67
     [Google Scholar]
  51. 51. 
    Magri DC, Brown GJ, McClean GD, Prasanna De Silva A. 2006. Communicating chemical congregation: a molecular and logic gate with three chemical inputs as a “lab-on-a-molecule” prototype. J. Am. Chem. Soc. 128:154950–51
     [Google Scholar]
  52. 52. 
    Uchiyama S, Kawai N, Silva APD, Iwai K. 2004. Fluorescent polymeric and logic gate with temperature and pH as inputs. J. Am. Chem. Soc. 126:103032–33
     [Google Scholar]
  53. 53. 
    Radhakrishnan K, Tripathy J, Raichur AM. 2013. Dual enzyme responsive microcapsules simulating an “Or” logic gate for biologically triggered drug delivery applications. Chem. Commun. 49:475390–92
     [Google Scholar]
  54. 54. 
    Szaciłowski K, Macyk W, Stochel G. 2006. Light-driven OR and XOR programmable chemical logic gates. J. Am. Chem. Soc. 128:144550–51
     [Google Scholar]
  55. 55. 
    Chen L, Zeng X, Dandapat A, Chi Y, Kim D 2015. Installing logic gates in permeability controllable polyelectrolyte-carbon nitride films for detecting proteases and nucleases. Anal. Chem. 87:178851–57
     [Google Scholar]
  56. 56. 
    Deng H-H, Wang F-F, Liu Y-H, Peng H-P, Li K-L et al. 2016. Label-free, resettable, and multi-readout logic gates based on chemically induced fluorescence switching of gold nanoclusters. J. Mater. Chem. C 4:297141–47
     [Google Scholar]
  57. 57. 
    Saghatelian A, Völcker NH, Guckian KM, Lin VS-Y, Ghadiri MR. 2003. DNA-based photonic logic gates: AND, NAND, and INHIBIT. J. Am. Chem. Soc. 125:2346–47
     [Google Scholar]
  58. 58. 
    Ryzhkov NV, Nesterov P, Mamchik NA, Yurchenko SO, Skorb EV. 2019. Localization of ion concentration gradients for logic operation. Front. Chem. 7:419
     [Google Scholar]
  59. 59. 
    Macyk W, Stochel G, Szaciłowski K. 2007. Photosensitization and the photocurrent switching effect in nanocrystalline titanium dioxide functionalized with iron(II) complexes: a comparative study. Chemistry 13:205676–87
     [Google Scholar]
  60. 60. 
    Szaciłowski K, Macyk W, Hebda M, Stochel G. 2006. Redox-controlled photosensitization of nanocrystalline titanium dioxide. Chem. Phys. Chem. 7:112384–91
     [Google Scholar]
  61. 61. 
    Furtado LFO, Alexiou ADP, Gonçalves L, Toma HE, Araki K. 2006. TiO2-based light-driven XOR/INH logic gates. Angew. Chem. Int. Ed. 45:193143–46
     [Google Scholar]
  62. 62. 
    Gawda S, Stochel G, Szaciłowski K. 2008. Photosensitization and photocurrent switching in carminic acid/titanium dioxide hybrid material. J. Phys. Chem. C 112:4819131–41
     [Google Scholar]
  63. 63. 
    Pilarczyk K, Kwolek P, Podborska A, Gawęda S, Oszajca M, Szaciłowski K 2016. Unconventional computing realized with hybrid materials exhibiting the photoelectrochemical photocurrent switching (PEPS) effect. Advances in Unconventional Computing 23 Emergence, Complexity and Computation Advances in Unconventional Computing A Adamatzky 429–67 Cham, Switz: Springer
     [Google Scholar]
  64. 64. 
    Ryzhkov NV, Yurova VY, Ulasevich SA, Skorb EV. 2020. Photoelectrochemical photocurrent switching effect on a pristine anodized Ti/TiO2 system as a platform for chemical logic devices. RSC Adv 10:2112355–59
     [Google Scholar]
  65. 65. 
    Li Z, Rosenbaum MA, Venkataraman A, Tam TK, Katz E, Angenent LT. 2011. Bacteria-based AND logic gate: a decision-making and self-powered biosensor. Chem. Commun. 47:113060–62
     [Google Scholar]
  66. 66. 
    Guliyev R, Ozturk S, Kostereli Z, Akkaya EU. 2011. From virtual to physical: integration of chemical logic gates. Angew. Chem. Int. Ed. 50:429826–31
     [Google Scholar]
  67. 67. 
    Braich RS. 2002. Solution of a 20-variable 3-SAT problem on a DNA computer. Science 296:5567499–502
     [Google Scholar]
  68. 68. 
    Smith LM, Corn RM, Condon AE, Lagally MG, Frutos AG et al. 1998. A surface-based approach to DNA computation. J. Comput. Biol. 5:2255–67
     [Google Scholar]
  69. 69. 
    Lin S. 1965. Computer solutions of the traveling salesman problem. Bell Syst. Tech. J. 44:102245–69
     [Google Scholar]
  70. 70. 
    Ouyang Q. 1997. DNA solution of the maximal clique problem. Science 278:5337446–49
     [Google Scholar]
  71. 71. 
    Faulhammer D, Cukras AR, Lipton RJ, Landweber LF. 2000. Molecular computation: RNA solutions to chess problems. PNAS 97:41385–89
     [Google Scholar]
  72. 72. 
    Bonnet J, Yin P, Ortiz ME, Subsoontorn P, Endy D. 2013. Amplifying genetic logic gates. Science 340:6132599–603
     [Google Scholar]
  73. 73. 
    Siuti P, Yazbek J, Lu TK. 2013. Synthetic circuits integrating logic and memory in living cells. Nat. Biotechnol. 31:5448–52
     [Google Scholar]
  74. 74. 
    Pode Z, Peri-Naor R, Georgeson JM, Ilani T, Kiss V et al. 2017. Protein recognition by a pattern-generating fluorescent molecular probe. Nat. Nanotechnol. 12:121161–68
     [Google Scholar]
  75. 75. 
    Rout B, Milko P, Iron MA, Motiei L, Margulies D. 2013. Authorizing multiple chemical passwords by a combinatorial molecular keypad lock. J. Am. Chem. Soc. 135:4115330–33
     [Google Scholar]
  76. 76. 
    Kim K-W, Bocharova V, Halámek J, Oh M-K, Katz E. 2010. Steganography and encrypting based on immunochemical systems. Biotechnol. Bioeng. 108:51100–7
     [Google Scholar]
  77. 77. 
    Ratner T, Reany O, Keinan E. 2009. Encoding and processing of alphanumeric information by chemical mixtures. Chem. Phys. Chem. 10:183303–9
     [Google Scholar]
  78. 78. 
    Nenashkina A, Koltsov S, Zaytseva E, Brunova A, Pantiukhin I, Skorb EV. 2020. Storage of information using periodic precipitation. ACS Omega 5:147809–14
     [Google Scholar]
  79. 79. 
    Rogers WB, Shih WM, Manoharan VN. 2016. Using DNA to program the self-assembly of colloidal nanoparticles and microparticles. Nat. Rev. Mater. 1:16008
     [Google Scholar]
  80. 80. 
    Kanaras AG, Wang Z, Bates AD, Cosstick R, Brust M. 2003. Towards multistep nanostructure synthesis: programmed enzymatic self-assembly of DNA/gold systems. Angew. Chem. Int. Ed. 42:2191–94
     [Google Scholar]
  81. 81. 
    Chen G, Gibson KJ, Liu D, Rees HC, Lee J-H et al. 2018. Regioselective surface encoding of nanoparticles for programmable self-assembly. Nat. Mater. 18:2169–74
     [Google Scholar]
  82. 82. 
    Loescher S, Walther A 2020. Supracolloidal self-assembly of divalent Janus 3D DNA origami via programmable multivalent host/guest interactions. Angew. Chem. Int. Ed. 59:145515–20
     [Google Scholar]
  83. 83. 
    Yin P, Choi HMT, Calvert CR, Pierce NA. 2008. Programming biomolecular self-assembly pathways. Nature 451:7176318–22
     [Google Scholar]
  84. 84. 
    Ong LL, Hanikel N, Yaghi OK, Grun C, Strauss MT et al. 2017. Programmable self-assembly of three-dimensional nanostructures from 10,000 unique components. Nature 552:768372–77
     [Google Scholar]
  85. 85. 
    Zhu H, Wang H, Shi B, Shangguan L, Tong W et al. 2019. Supramolecular peptide constructed by molecular Lego allowing programmable self-assembly for photodynamic therapy. Nat. Commun. 10:2412
     [Google Scholar]
  86. 86. 
    Xing P, Phua SZF, Wei X, Zhao Y. 2018. Programmable multicomponent self-assembly based on aromatic amino acids. Adv. Mater. 30:491805175
     [Google Scholar]
  87. 87. 
    Mout R, Tonga GY, Wang L-S, Ray M, Roy T, Rotello VM. 2017. Programmed self-assembly of hierarchical nanostructures through protein-nanoparticle coengineering. ACS Nano 11:43456–62
     [Google Scholar]
  88. 88. 
    Taniguchi Y, Sazali MAB, Kobayashi Y, Arai N, Kawai T, Nakashima T. 2017. Programmed self-assembly of branched nanocrystals with an amphiphilic surface pattern. ACS Nano 11:99312–20
     [Google Scholar]
  89. 89. 
    Morphew D, Shaw J, Avins C, Chakrabarti D. 2018. Programming hierarchical self-assembly of patchy particles into colloidal crystals via colloidal molecules. ACS Nano 12:32355–64
     [Google Scholar]
  90. 90. 
    Tóth-Szeles E, Horváth J, Holló G, Szűcs R, Nakanishi H, Lagzi I. 2017. Chemically coded time-programmed self-assembly. Mol. Syst. Des. Eng. 2:3274–82
     [Google Scholar]
  91. 91. 
    Zhang X, Zou J, Tamhane K, Kobzeff FF, Fang J. 2010. Self-assembly of pH-switchable spiral tubes: supramolecular chemical springs. Small 6:2217–20
     [Google Scholar]
  92. 92. 
    Liu H, Li C, Liu H, Liu S. 2009. pH-responsive supramolecular self-assembly of well-defined zwitterionic ABC miktoarm star terpolymers. Langmuir 25:84724–34
     [Google Scholar]
  93. 93. 
    Ghosh A, Haverick M, Stump K, Yang X, Tweedle MF, Goldberger JE. 2012. Fine-tuning the pH trigger of self-assembly. J. Am. Chem. Soc. 134:83647–50
     [Google Scholar]
  94. 94. 
    Fomina N, Johnson CA, Maruniak A, Bahrampour S, Lang C et al. 2016. An electrochemical platform for localized pH control on demand. Lab Chip 16:122236–44
     [Google Scholar]
  95. 95. 
    Ryzhkov NV, Mamchik NA, Skorb EV. 2019. Electrochemical triggering of lipid bilayer lift-off oscillation at the electrode interface. J. R. Soc. Interface 16:15020180626
     [Google Scholar]
  96. 96. 
    Rattay F. 1999. The basic mechanism for the electrical stimulation of the nervous system. Neuroscience 89:2335–46
     [Google Scholar]
  97. 97. 
    Kléber AG, Rudy Y 2004. Basic mechanisms of cardiac impulse propagation and associated arrhythmias. Physiol. Rev. 84:2431–88
     [Google Scholar]
  98. 98. 
    Abrams DM, Strogatz SH. 2004. Chimera states for coupled oscillators. Phys. Rev. Lett. 93:17174102
     [Google Scholar]
  99. 99. 
    Hankins MJ, Gáspár V, Kiss IZ. 2019. Abrupt and gradual onset of synchronized oscillations due to dynamical quorum sensing in the single-cathode multi-anode nickel electrodissolution system. Chaos 29:3033114
     [Google Scholar]
  100. 100. 
    Sebek M, Kawamura Y, Nott AM, Kiss IZ. 2019. Anti-phase collective synchronization with intrinsic in-phase coupling of two groups of electrochemical oscillators. Philos. Trans. R. Soc. A 377:216020190095
     [Google Scholar]
  101. 101. 
    dos Santos CGP, Machado EG, Kiss IZ, Nagao R. 2019. Investigation of the oscillatory electrodissolution of the nickel-iron alloy. J. Phys. Chem. C 123:3924087–94
     [Google Scholar]
  102. 102. 
    Sebek M, Kiss I. 2019. Plasticity facilitates pattern selection of networks of chemical oscillations. Chaos 29:9083117
     [Google Scholar]
  103. 103. 
    Sebek M, Kiss IZ. 2018. Spatiotemporal patterns on a ring network of oscillatory electrochemical reaction with negative global feedback. Israel J. Chem. 58:6–7753–61
     [Google Scholar]
  104. 104. 
    Ocampo-Espindola JL, Bick C, Kiss IZ. 2019. Weak chimeras in modular electrochemical oscillator networks. Front. Appl. Math. Stat. 5:36
     [Google Scholar]
  105. 105. 
    Guzowski J, Gizynski K, Gorecki J, Garstecki P. 2016. Microfluidic platform for reproducible self-assembly of chemically communicating droplet networks with predesigned number and type of the communicating compartments. Lab Chip 16:4764–72
     [Google Scholar]
  106. 106. 
    Torbensen K, Ristori S, Rossi F, Abou-Hassan A. 2017. Tuning the chemical communication of oscillating microdroplets by means of membrane composition. J. Phys. Chem. C 121:2413256–64
     [Google Scholar]
  107. 107. 
    Totz JF, Rode J, Tinsley MR, Showalter K, Engel H. 2017. Spiral wave chimera states in large populations of coupled chemical oscillators. Nat. Phys. 14:3282–85
     [Google Scholar]
  108. 108. 
    Nkomo S, Tinsley MR, Showalter K. 2016. Chimera and chimera-like states in populations of nonlocally coupled homogeneous and heterogeneous chemical oscillators. Chaos 26:9094826
     [Google Scholar]
  109. 109. 
    Awal NM, Bullara D, Epstein IR. 2019. The smallest chimera: periodicity and chaos in a pair of coupled chemical oscillators. Chaos 29:1013131
     [Google Scholar]
  110. 110. 
    Steinbock O, Kettunen P, Showalter K. 1996. Chemical wave logic gates. J. Phys. Chem. 100:4918970–75
     [Google Scholar]
  111. 111. 
    Gizynski K, Gorecki J. 2017. Cancer classification with a network of chemical oscillators. Phys. Chem. Chem. Phys. 19:4228808–19
     [Google Scholar]
  112. 112. 
    Gizynski K, Gorecki J. 2017. Chemical memory with states coded in light controlled oscillations of interacting Belousov-Zhabotinsky droplets. Phys. Chem. Chem. Phys. 19:96519–31
     [Google Scholar]
  113. 113. 
    Vanag VK. 2019. Hierarchical network of pulse coupled chemical oscillators with adaptive behavior: chemical neurocomputer. Chaos 29:8083104
     [Google Scholar]
  114. 114. 
    Friess M, Hammann J, Unichenko P, Luhmann HJ, White R, Kirischuk S. 2016. Intracellular ion signaling influences myelin basic protein synthesis in oligodendrocyte precursor cells. Cell Calcium 60:5322–30
     [Google Scholar]
  115. 115. 
    Lv M, Zhou Y, Chen X, Han L, Wang L, Lu XL. 2017. Calcium signaling of in situ chondrocytes in articular cartilage under compressive loading: roles of calcium sources and cell membrane ion channels. J. Orthop. Res. 36:2730–38
     [Google Scholar]
  116. 116. 
    Voolstra O, Huber A. 2019. Ca2+ signaling in Drosophila photoreceptor cells. Calcium Signaling MS Islam pp.857–79 Adv. Exp. Med. Biol. Cham Switz.: Springer Int.
     [Google Scholar]
  117. 117. 
    Franklin BM, Voss SR, Osborn JL. 2017. Ion channel signaling influences cellular proliferation and phagocyte activity during axolotl tail regeneration. Mech. Dev. 146:42–54
     [Google Scholar]
  118. 118. 
    Lüscher BP, Vachel L, Ohana E, Muallem S. 2020. Cl− as a bona fide signaling ion. Am. J. Physiol. Cell Physiol. 318:1C125–36
     [Google Scholar]
  119. 119. 
    Maret W. 2017. Zinc in cellular regulation: the nature and significance of “zinc signals. .” Int. J. Mol. Sci. 18:112285
     [Google Scholar]
  120. 120. 
    Ma J, Zhao N, Zhu D. 2016. Bioabsorbable zinc ion induced biphasic cellular responses in vascular smooth muscle cells. Sci. Rep. 6:26661
     [Google Scholar]
  121. 121. 
    Lazarou TS, Buccella D. 2020. Advances in imaging of understudied ions in signaling: a focus on magnesium. Curr. Opin. Chem. Biol. 57:27–33
     [Google Scholar]
  122. 122. 
    Liu J, Prindle A, Humphries J, Gabalda-Sagarra M, Asally M et al. 2015. Metabolic co-dependence gives rise to collective oscillations within biofilms. Nature 523:7562550–54
     [Google Scholar]
  123. 123. 
    Plett TS, Cai W, Thai ML, Vlassiouk IV, Penner RM, Siwy ZS. 2017. Solid-state ionic diodes demonstrated in conical nanopores. J. Phys. Chem. C 121:116170–76
     [Google Scholar]
  124. 124. 
    Mathwig K, Aaronson BDB, Marken F. 2017. Ionic transport in microhole fluidic diodes based on asymmetric ionomer film deposits. ChemElectroChem 5:6897–901
     [Google Scholar]
  125. 125. 
    Aaronson BDB, He D, Madrid E, Johns MA, Scott JL et al. 2017. Ionic diodes based on regenerated α-cellulose films deposited asymmetrically onto a microhole. ChemistrySelect 2:3871–75
     [Google Scholar]
  126. 126. 
    Lin C-Y, Combs C, Su Y-S, Yeh L-H, Siwy ZS. 2019. Rectification of concentration polarization in mesopores leads to high conductance ionic diodes and high performance osmotic power. J. Am. Chem. Soc. 141:83691–98
     [Google Scholar]
  127. 127. 
    Zhao Y, Dai S, Chu Y, Wu X, Huang J. 2018. A flexible ionic synaptic device and diode-based aqueous ion sensor utilizing asymmetric polyelectrolyte distribution. Chem. Commun. 54:598186–89
     [Google Scholar]
  128. 128. 
    Gao J, Koltonow AR, Raidongia K, Beckerman B, Boon N et al. 2018. Kirigami nanofluidics. Mater. Chem. Front 2:3475–82
     [Google Scholar]
  129. 129. 
    Kong Y, Fan X, Zhang M, Hou X, Liu Z et al. 2013. Nanofluidic diode based on branched alumina nanochannels with tunable ionic rectification. ACS Appl. Mater. Interfaces 5:167931–36
     [Google Scholar]
  130. 130. 
    Yan R, Liang W, Fan R, Yang P 2009. Nanofluidic diodes based on nanotube heterojunctions. Nano Lett 9:113820–25
     [Google Scholar]
  131. 131. 
    Hou Y, Zhou Y, Yang L, Li Q, Zhang Y et al. 2016. Flexible ionic diodes for low-frequency mechanical energy harvesting. Adv. Energy Mater. 7:51601983
     [Google Scholar]
  132. 132. 
    Lee H-R, Woo J, Han SH, Lim S-M, Lim S et al. 2018. A stretchable ionic diode from copolyelectrolyte hydrogels with methacrylated polysaccharides. Adv. Funct. Mater. 29:41806909
     [Google Scholar]
  133. 133. 
    Lim S-M, Yoo H, Oh M-A, Han SH, Lee H-R et al. 2019. Ion-to-ion amplification through an open-junction ionic diode. PNAS 116:2813807–15
     [Google Scholar]
  134. 134. 
    Duan J, Xie W, Yang P, Li J, Xue G et al. 2018. Tough hydrogel diodes with tunable interfacial adhesion for safe and durable wearable batteries. Nano Energy 48:569–74
     [Google Scholar]
  135. 135. 
    Wang Y, Zhai J. 2019. Cell junction proteins-mimetic artificial nanochannel system: basic logic gates implemented by nanofluidic diodes. Langmuir 35:83171–75
     [Google Scholar]
  136. 136. 
    Ali M, Ramirez P, Nasir S, Cervera J, Mafe S, Ensinger W. 2019. Ionic circuitry with nanofluidic diodes. Soft Matter 15:479682–89
     [Google Scholar]
  137. 137. 
    Putra BR, Carta M, Malpass-Evans R, McKeown NB, Marken F. 2017. Potassium cation induced ionic diode blocking for a polymer of intrinsic microporosity | nafion “heterojunction” on a microhole substrate. Electrochim. Acta 258:807–13
     [Google Scholar]
  138. 138. 
    Rong Y, Song Q, Mathwig K, Madrid E, He D et al. 2016. pH-induced reversal of ionic diode polarity in 300 nm thin membranes based on a polymer of intrinsic microporosity. Electrochem. Commun. 69:41–45
     [Google Scholar]
  139. 139. 
    Wang L, Feng Y, Zhou Y, Jia M, Wang G et al. 2017. Photo-switchable two-dimensional nanofluidic ionic diodes. Chem. Sci. 8:64381–86
     [Google Scholar]
  140. 140. 
    Xiao K, Chen L, Chen R, Heil T, Lemus SDC et al. 2019. Artificial light-driven ion pump for photoelectric energy conversion. Nat. Commun. 10:74
     [Google Scholar]
  141. 141. 
    Li J, An P, Qin C, Sun C-L, Sun M et al. 2020. Bioinspired dual-responsive nanofluidic diodes by poly-l-lysine modification. ACS Omega 5:94501–6
     [Google Scholar]
  142. 142. 
    Ali M, Ramirez P, Mafé S, Neumann R, Ensinger W. 2009. A pH-tunable nanofluidic diode with a broad range of rectifying properties. ACS Nano 3:3603–8
     [Google Scholar]
  143. 143. 
    Zhang Z, Wang L, Wang J, Jiang X, Li X et al. 2012. Mesoporous silica-coated gold nanorods as a light-mediated multifunctional theranostic platform for cancer treatment. Adv. Mater. 24:111418–23
     [Google Scholar]
  144. 144. 
    Lin C-Y, Ma T, Siwy ZS, Balme S, Hsu J-P. 2019. Tunable current rectification and selectivity demonstrated in nanofluidic diodes through kinetic functionalization. J. Phys. Chem. Lett. 11:160–66
     [Google Scholar]
  145. 145. 
    Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W. 2010. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett 10:41297–301
     [Google Scholar]
  146. 146. 
    Schmitt R, Kubicek M, Sediva E, Trassin M, Weber MC et al. 2018. Accelerated ionic motion in amorphous memristor oxides for nonvolatile memories and neuromorphic computing. Adv. Funct. Mater. 29:51804782
     [Google Scholar]
  147. 147. 
    Lee J, Du C, Sun K, Kioupakis E, Lu WD. 2016. Tuning ionic transport in memristive devices by graphene with engineered nanopores. ACS Nano 10:33571–79
     [Google Scholar]
  148. 148. 
    Tuszynski JA, Friesen D, Freedman H, Sbitnev VI, Kim H et al. 2020. Microtubules as sub-cellular memristors. Sci. Rep. 10:2108
     [Google Scholar]
  149. 149. 
    Zhang P, Xia M, Zhuge F, Zhou Y, Wang Z et al. 2019. Nanochannel-based transport in an interfacial memristor can emulate the analog weight modulation of synapses. Nano Lett 19:74279–86
     [Google Scholar]
  150. 150. 
    Sheng Q, Xie Y, Li J, Wang X, Xue J 2017. Transporting an ionic-liquid/water mixture in a conical nanochannel: a nanofluidic memristor. Chem. Commun. 53:456125–27
     [Google Scholar]
  151. 151. 
    Kim N, Thomas MR, Bergholt MS, Pence IJ, Seong H et al. 2020. Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting. Nat. Commun. 11:207
     [Google Scholar]
  152. 152. 
    González-Morales D, Valencia A, Díaz-Nuñez A, Fuentes-Estrada M, López-Santos O, García-Beltrán O. 2020. Development of a low-cost UV-Vis spectrophotometer and its application for the detection of mercuric ions assisted by chemosensors. Sensors 20:3906
     [Google Scholar]
  153. 153. 
    Wang X, Huang Z, Chen J, Luo Z, Xu Y, Duan Y. 2019. A colorimetric sensing platform based on site-specific endonuclease IV-aided signal amplification for the detection of DNA related to the human immunodeficiency virus. Anal. Methods 11:162190–96
     [Google Scholar]
  154. 154. 
    Nikolaev KG, Ermakov SS, Offenhäusser A, Mourzina Y. 2017. Nonenzymatic determination of glucose on electrodes prepared by directed electrochemical nanowire assembly (DENA). J. Anal. Chem. 72:4371–74
     [Google Scholar]
  155. 155. 
    Nikolaev KG, Maybeck V, Neumann E, Ermakov SS, Ermolenko YE et al. 2017. Bimetallic nanowire sensors for extracellular electrochemical hydrogen peroxide detection in HL-1 cell culture. J. Solid State Electrochem. 22:41023–35
     [Google Scholar]
  156. 156. 
    Stekolshchikova AA, Radaev AV, Orlova OY, Nikolaev KG, Skorb EV. 2019. Thin and flexible ion sensors based on polyelectrolyte multilayers assembled onto the carbon adhesive tape. ACS Omega 4:1315421–27
     [Google Scholar]
  157. 157. 
    Zhang C, Su Y, Hu S, Jin K, Jie Y et al. 2020. An impedance sensing platform for monitoring heterogeneous connectivity and diagnostics in lab-on-a-chip systems. ACS Omega 5:105098–104
     [Google Scholar]
  158. 158. 
    Lee W, Someya T. 2019. Emerging trends in flexible active multielectrode arrays. Chem. Mater. 31:176347–58
     [Google Scholar]
  159. 159. 
    Criscuolo F. 2020. Wearable multi-electrode platform for ion sensing. Thesis Swiss Fed. Inst. Technol. Laussane Laussane, Switz:.
     [Google Scholar]
  160. 160. 
    Stanley-Marbell P, Rinard M. 2020. Warp: a hardware platform for efficient multimodal sensing with adaptive approximation. IEEE Micro 40:57–66
     [Google Scholar]
  161. 161. 
    Al-Rawhani MA, Mitra S, Barrett MP, Cochran S, Cumming DRS et al. 2020. Multimodal integrated sensor platform for rapid biomarker detection. IEEE Trans. Biomed. Eng. 67:2614–23
     [Google Scholar]
  162. 162. 
    Hanitra IN, Criscuolo F, Pankratova N, Carrara S, Micheli GD. 2020. Multichannel front-end for electrochemical sensing of metabolites, drugs, and electrolytes. IEEE Sens. J. 20:73636–45
     [Google Scholar]
  163. 163. 
    Manickam P, Kanagavel V, Sonawane A, Thipperudraswamy SP, Bhansali S 2019. Electrochemical systems for healthcare applications. Bioelectrochemical Interface Engineering, ed. RN Krishnaraj, RK Sani 385–409 Hoboken, NJ: John Wiley & Sons
     [Google Scholar]
  164. 164. 
    Nikolaev KG, Kalmykov EV, Shavronskaya DO, Nikitina AA, Stekolshchikova AA et al. 2020. ElectroSens platform with a polyelectrolyte-based carbon fiber sensor for point-of-care analysis of Zn in blood and urine. ACS Omega 5:3018987–94
     [Google Scholar]
  165. 165. 
    Hong YJ, Lee H, Kim J, Lee M, Choi HJ et al. 2018. Multifunctional wearable system that integrates sweat-based sensing and vital-sign monitoring to estimate pre-/post-exercise glucose levels. Adv. Funct. Mater. 28:471805754
     [Google Scholar]
  166. 166. 
    Martín A, Kim J, Kurniawan JF, Sempionatto JR, Moreto JR et al. 2017. Epidermal microfluidic electrochemical detection system: enhanced sweat sampling and metabolite detection. ACS Sens 2:121860–68
     [Google Scholar]
  167. 167. 
    Oh SY, Hong SY, Jeong YR, Yun J, Park H et al. 2018. Skin-attachable, stretchable electrochemical sweat sensor for glucose and pH detection. ACS Appl. Mater. Interfaces 10:1613729–40
     [Google Scholar]
  168. 168. 
    Pasha SK, Kaushik A, Vasudev A, Snipes SA, Bhansali S. 2013. Electrochemical immunosensing of saliva cortisol. J. Electrochem. Soc. 161:2B3077–82
     [Google Scholar]
  169. 169. 
    Yin K, Pandian V, Kadimisetty K, Zhang X, Ruiz C et al. 2020. Real-time colorimetric quantitative molecular detection of infectious diseases on smartphone-based diagnostic platform. Sci. Rep. 10:9009
     [Google Scholar]
  170. 170. 
    Shi W, Li J, Wu J, Wei Q, Chen C et al. 2020. An electrochemical biosensor based on multi-wall carbon nanotube-modified screen-printed electrode immobilized by uricase for the detection of salivary uric acid. Anal. Bioanal. Chem. 412:267275–83
     [Google Scholar]
  171. 171. 
    Magar HS, Abbas MN, MB Ali, Ahmed MA. 2020. Picomolar-sensitive impedimetric sensor for salivary calcium analysis at POC based on Sam of Schiff base-modified gold electrode. J. Solid State Electrochem. 24:3723–37
     [Google Scholar]
  172. 172. 
    Tiegs AW, Scott RT. 2020. Evaluation of fertilization, usable blastocyst development and sustained implantation rates according to intracytoplasmic sperm injection operator experience. Reprod. BioMed. Online 41:119–27
     [Google Scholar]
  173. 173. 
    Gosalvez J, Tvrda E, Agarwal A. 2017. Free radical and superoxide reactivity detection in semen quality assessment: past, present, and future. J. Assist. Reprod. Genet. 34:6697–707
     [Google Scholar]
  174. 174. 
    Blanco E, Vázquez L, del Pozo M, Roy R, Petit-Domínguez MD et al. 2020. Evaluation of oxidative stress: nanoparticle-based electrochemical sensors for hydrogen peroxide determination in human semen samples. Bioelectrochemistry 135:107581
     [Google Scholar]
  175. 175. 
    Yang X, Forouzan O, Brown TP, Shevkoplyas SS. 2012. Integrated separation of blood plasma from whole blood for microfluidic paper-based analytical devices. Lab Chip 12:2274–80
     [Google Scholar]
  176. 176. 
    Helton KL, Nelson KE, Fu E, Yager P. 2008. Conditioning saliva for use in a microfluidic biosensor. Lab Chip 8:111847–51
     [Google Scholar]
  177. 177. 
    Castro-López V, Elizalde J, Pacek M, Hijona E, Bujanda L. 2014. A simple and portable device for the quantification of TNF-α in human plasma by means of on-chip magnetic bead-based proximity ligation assay. Biosens. Bioelectron. 54:499–505
     [Google Scholar]
  178. 178. 
    Fei C, Ren C, Wang Y, Li W, Yin F et al. 2021. Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods. Sci. Rep. 11:1849
     [Google Scholar]
  179. 179. 
    Diouf A, Aghoutane Y, Burhan H, Sen F, Bouchikhi B, El Bari N 2021. Tramadol sensing in non-invasive biological fluids using a voltammetric electronic tongue and an electrochemical sensor based on biomimetic recognition. Int. J. Pharm. 593:120114
     [Google Scholar]
  180. 180. 
    Jiao T, Hassan MM, Zhu J, Ali S, Ahmad W et al. 2021. Quantification of deltamethrin residues in wheat by Ag@ZnO NFs-based surface-enhanced Raman spectroscopy coupling chemometric models. Food Chem 337:127652
     [Google Scholar]
  181. 181. 
    Novati G, de Laroussilhe HL, Koumoutsakos P. 2021. Automating turbulence modelling by multi-agent reinforcement learning. Nat. Mach. Intell. 3:87–96
     [Google Scholar]
  182. 182. 
    Nazarenko DV, Rodin IA, Shpigun OA. 2019. The use of machine learning in the analytical control of the preparations of medicinal plants. Inorg. Mater. 55:141428–38
     [Google Scholar]
  183. 183. 
    Picache JA, May JC, McLean JA. 2020. Chemical class prediction of unknown biomolecules using ion mobility-mass spectrometry and machine learning: supervised inference of feature taxonomy from ensemble randomization. Anal. Chem. 92:1510759–67
     [Google Scholar]
  184. 184. 
    Shang C, Yang F, Huang D, Lyu W. 2014. Data-driven soft sensor development based on deep learning technique. J. Process Control 24:3223–33
     [Google Scholar]
  185. 185. 
    Kennedy GF, Zhang J, Bond AM. 2019. Automatically identifying electrode reaction mechanisms using deep neural networks. Anal. Chem. 91:1912220–27
     [Google Scholar]
  186. 186. 
    Peris-Díaz MD, Richtera L, Zitka O, Krężel A, Adam V. 2020. A chemometric-assisted voltammetric analysis of free and Zn(II)-loaded metallothionein-3 states. Bioelectrochemistry 134:107501
     [Google Scholar]
  187. 187. 
    Dean SN, Shriver-Lake LC, Stenger DA, Erickson JS, Golden JP, Trammell SA. 2019. Machine learning techniques for chemical identification using cyclic square wave voltammetry. Sensors 19:2392
     [Google Scholar]
  188. 188. 
    Aliramezani M, Norouzi A, Koch CR. 2020. A grey-box machine learning based model of an electrochemical gas sensor. Sens. Actuators B 321:128414
     [Google Scholar]
  189. 189. 
    Lu H, Li H, Liu T, Fan Y, Yuan Y et al. 2019. Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes. Sci. Total Environ. 694:133591
     [Google Scholar]
  190. 190. 
    Bond AM. 2020. A perceived paucity of quantitative studies in the modern era of voltammetry: prospects for parameterisation of complex reactions in Bayesian and machine learning frameworks. J. Solid State Electrochem. 24:92041–50
     [Google Scholar]
  191. 191. 
    Boucheikhchoukh A, Thibault J, Fauteux-Lefebvre C. 2020. Catalyst design using artificial intelligence: SO2 to SO3 case study. Can. J. Chem. Eng. 98:92016–31
     [Google Scholar]
  192. 192. 
    Chen Y, Huang Y, Cheng T, Goddard WA 2019. Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141:2911651–57
     [Google Scholar]
  193. 193. 
    Gu GH, Choi C, Lee Y, Situmorang AB, Noh J et al. 2020. Progress in computational and machine-learning methods for heterogeneous small-molecule activation. Adv. Mater. 32:351907865
     [Google Scholar]
  194. 194. 
    Song J, Zheng Y, Huang M, Wu L, Wang W et al. 2019. A sequential multidimensional analysis algorithm for aptamer identification based on structure analysis and machine learning. Anal. Chem. 92:43307–14
     [Google Scholar]
  195. 195. 
    Qi Y, Bar-Joseph Z, Klein-Seetharaman J. 2006. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 63:3490–500
     [Google Scholar]
  196. 196. 
    Ivanov AS, Nikolaev KG, Stekolshchikova AA, Tesfatsion WT, Yurchenko SO et al. 2020. Tick-borne encephalitis electrochemical detection by multilayer perceptron on liquid metal interface. ACS Appl. Bio Mater. 3:117352–56
     [Google Scholar]
  197. 197. 
    Nikolaev KG, Ermolenko YE, Offenhäusser A, Ermakov SS, Mourzina YG. 2018. Multisensor systems by electrochemical nanowire assembly for the analysis of aqueous solutions. Front. Chem. 6:256
     [Google Scholar]
  198. 198. 
    Kisner A, Heggen M, Mayer D, Simon U, Offenhäusser A, Mourzina Y. 2014. Probing the effect of surface chemistry on the electrical properties of ultrathin gold nanowire sensors. Nanoscale 6:105146–55
     [Google Scholar]
  199. 199. 
    Dan B, Wingfield TB, Evans JS, Mirri F, Pint CL et al. 2011. Templating of self-alignment patterns of anisotropic gold nanoparticles on ordered SWNT macrostructures. ACS Appl. Mater. Interfaces 3:93718–24
     [Google Scholar]
  200. 200. 
    Duan X, Niu C, Sahi V, Chen J, Parce JW et al. 2003. High-performance thin-film transistors using semiconductor nanowires and nanoribbons. Nature 425:6955274–78
     [Google Scholar]
  201. 201. 
    Messer B, Song JH, Yang P 2000. Microchannel networks for nanowire patterning. J. Am. Chem. Soc. 122:4110232–33
     [Google Scholar]
  202. 202. 
    Picollo F, Battiato A, Bernardi E, Plaitano M, Franchino C et al. 2016. All-carbon multi-electrode array for real-time in vitro measurements of oxidizable neurotransmitters. Sci. Rep. 6:20682
     [Google Scholar]
  203. 203. 
    Guitchounts G, Cox D. 2020. 64-Channel carbon fiber electrode arrays for chronic electrophysiology. Sci. Rep. 10:3830
     [Google Scholar]
  204. 204. 
    Chen Y-H, Beeck MD, Vanderheyden L, Carrette E, Mihajlović V et al. 2014. Soft, comfortable polymer dry electrodes for high quality ECG and EEG recording. Sensors 14:1223758–80
     [Google Scholar]
  205. 205. 
    Grozea C, Voinescu CD, Fazli S. 2011. Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications. J. Neural Eng. 8:2025008
     [Google Scholar]
  206. 206. 
    Ivanov AS, Nikolaev KG, Novikov AS, Yurchenko SO, Novoselov KS et al. 2021. Programmable soft-matter electronics. J. Phys. Chem. Lett. 12:72017–22
     [Google Scholar]
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Infochemistry and the Future of Chemical Information Processing
Annual Review of Chemical and Biomolecular Engineering 12, 63 (2021); https://doi.org/10.1146/annurev-chembioeng-122120-023514
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