Advances in Soft Bioelectronics for Brain Research and Clinical Neuroengineering

Abstract

Advances in bioelectronics for neuroscience and neuroengineering have enabled continuous monitoring of electrophysiological signals and feedback modulation of abnormal brain activities. Despite such progress, there remain issues in terms of long-term high-quality neural interfacing, mainly owing to inherent mechanical, chemical, and electrical mismatches between the device and the brain tissue. New approaches, therefore, are required to address these discrepancies between the biotic and abiotic system. This review introduces technological advances that potentially solve such issues by using soft materials and devices. Specifically, we summarize recent progress in soft materials, such as nanoscale materials, conductive polymers, functionalized hydrogels, and stretchable conductive nanocomposites. These unconventional materials, combined with customized processing techniques and device designs, provide novel soft device solutions for brain science and clinical neuroengineering. Recent advances in bioelectronics, such as skin-mounted electroencephalography sensors, multi-channel neural probes, and closed-loop deep brain stimulators, have enabled electrophysiological brain activities to be both monitored and modulated. Despite this remarkable progress, major challenges remain, which stem from the inherent mechanical, chemical, and electrical differences that exist between brain tissues and bioelectronics. New approaches are therefore required to address these mismatches between biotic and abiotic systems. Here, we review recent technological advances that minimize such mismatches by using unconventional soft materials, such as silicon/metal nanowires, functionalized hydrogels, and stretchable conductive nanocomposites, as well as customized fabrication processes and novel device designs. The resulting novel, soft bioelectronic devices provide new opportunities for brain research and clinical neuroengineering. Recent advances in bioelectronics, such as skin-mounted electroencephalography sensors, multi-channel neural probes, and closed-loop deep brain stimulators, have enabled electrophysiological brain activities to be both monitored and modulated. Despite this remarkable progress, major challenges remain, which stem from the inherent mechanical, chemical, and electrical differences that exist between brain tissues and bioelectronics. New approaches are therefore required to address these mismatches between biotic and abiotic systems. Here, we review recent technological advances that minimize such mismatches by using unconventional soft materials, such as silicon/metal nanowires, functionalized hydrogels, and stretchable conductive nanocomposites, as well as customized fabrication processes and novel device designs. The resulting novel, soft bioelectronic devices provide new opportunities for brain research and clinical neuroengineering. Brain diseases and disorders, including neurodegenerative, psychiatric, oncological, and neurodevelopmental, as well as injuries that require neurorehabilitation, have highlighted a clinical role and need for neurotechnologies that can monitor and modulate brain activity. Such technologies might also have wider clinical applications, given that electrical signals from the brain coordinate peripheral nerves and electrically active organs, including the heart,1Levy M.N. Martin P.J. Autonomic neural control of cardiac function.in: Sperelakis N. Physiology and Pathophysiology of the Heart. Kluwer Academic Publishers, 1989: 361-379Crossref Google Scholar muscles,2Houk J.C. Rymer W.Z. Neural control of muscle length and tension.in: Comprehensive Physiology Supplement 2. Handbook of Physiology. The Nervous System, Motor Control. Wiley, 2011https://doi.org/10.1002/cphy.cp010208Crossref Google Scholar gastrointestinal organs,3Browning K.N. Travagli R.A. Central nervous system control of gastrointestinal motility and secretion and modulation of gastrointestinal functions.Compr. Physiol. 2014; 4: 1339-1368Crossref PubMed Scopus (131) Google Scholar and endocrine organs.4Ulrich-Lai Y.M. Herman J.P. Neural regulation of endocrine and autonomic stress responses.Nat. Rev. Neurosci. 2009; 10: 397-409Crossref PubMed Scopus (1595) Google Scholar However, a key challenge for neurotechnologies is to provide faithful, minimally invasive, and long-term recordings of electrical signals from the nervous system in often-changing physiological conditions, for example during different developmental, behavioral, and neurological states, and in pathological or trauma conditions. Bioelectronic technologies to date have facilitated our understanding of abnormal electrophysiological signals as well as the recognition of subtle pathological symptoms, the modulation of epileptic neuronal activities, and the treatment of various intractable neurological diseases. They include recording devices for skin-based electroencephalography (EEG),5Chen Y. Zhang Y. Liang Z. Cao Y. Han Z. Feng X. Flexible inorganic bioelectronics.Npj Flex. Electron. 2020; 4: 2Crossref Scopus (8) Google Scholar epicortical electrocorticography (ECoG),6Shi Z. Zheng F. Zhou Z. Li M. Fan Z. Ye H. Zhang S. Xiao T. Chen L. Tao T.H. et al.Silk-enabled conformal multifunctional bioelectronics for investigation of spatiotemporal epileptiform activities and multimodal neural encoding/decoding.Adv. Sci. 2019; 6: 1801617Crossref Scopus (9) Google Scholar,7Vomero M. Gueli C. Zucchini E. Fadiga L. Erhardt J.B. Sharma S. Stieglitz T. Flexible bioelectronic devices based on micropatterned monolithic carbon fiber mats.Adv. Mater. Technol. 2020; 5: 1900713Crossref Scopus (3) Google Scholar and intracortical encephalography (ICE).8Maynard E.M. Nordhausen C.T. Normann R.A. The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces.Electroencephalogr. Clin. Neurophysiol. 1997; 102: 228-239Abstract Full Text PDF PubMed Scopus (358) Google Scholar,9Szostak K.M. Grand L. Constandinou T.G. Neural interfaces for intracortical recording: requirements, fabrication methods, and characteristics.Front. Neurosci. 2017; 11: 665Crossref PubMed Scopus (29) Google Scholar Optogenetic10Kim T.-I. McCall J.G. Jung Y.H. Huang X. Siuda E.R. Li Y. Song J. Song Y.M. Pao H.A. Kim R.H. et al.Injectable, cellular-scale optoelectronics with applications for wireless optogenetics.Science. 2013; 340: 211-216Crossref PubMed Scopus (673) Google Scholar and thermal11Chen R. Romero G. Christiansen M.G. Mohr A. Anikeeva P. Wireless magnetothermal deep brain stimulation.Science. 2015; 347: 1477-1480Crossref PubMed Google Scholar stimulation devices have also been devised as new paradigms for neural modulation. The integration of wireless device components and the miniaturization of an entire implantable system have also significantly accelerated such advances.12Gutruf P. Krishnamurthi V. Vázquez-Guardado A. Xie Z. Banks A. Su C.J. Xu Y. Haney C.R. Waters E.A. Kandela I. et al.Fully implantable optoelectronic systems for battery-free, multimodal operation in neuroscience research.Nat. Electron. 2018; 1: 652-660Crossref Scopus (28) Google Scholar Despite these advances, maintaining the long-term biocompatibility, reliability, and stability of such bioelectronic devices in vivo remains a key challenge. These issues are largely due to the different mechanical characteristics, called “mechanical mismatches,” that exist between biological tissue and the materials used in bioelectronic devices. In addition, there are notable mismatches between the chemical13Hong Y.J. Jeong H. Cho K.W. Lu N. Kim D.H. Wearable and implantable devices for cardiovascular healthcare: from monitoring to therapy based on flexible and stretchable electronics.Adv. Funct. Mater. 2019; 29: 1808247Crossref Scopus (0) Google Scholar and electrical14Butson C.R. McIntyre C.C. Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation.Clin. Neurophysiol. 2005; 116: 2490-2500Crossref PubMed Scopus (217) Google Scholar,15McCreery D.B. Agnew W.F. Yuen T.G.H. Bullara L.A. Comparison of neural damage induced by electrical stimulation with faradaic and capacitor electrodes.Ann. Biomed. Eng. 1988; 16: 463-481Crossref PubMed Scopus (114) Google Scholar properties of these devices and brain tissue; while brain tissue consists of organic structures that have a high water content in which ions control signal transmission, bioelectronic devices are typically composed of dry inorganic materials in which electrons serve as information transmitters. In recent years, the limitations of conventional bioelectronics have been addressed by engineering better materials and by designing advanced devices, heralding the advent of soft bioelectronics. In this review, we discuss how the development of new soft bioelectronics aims to mitigate these limitations, focusing on material designs, fabrication, and device choice for diverse applications. We also describe how soft bioelectronics creates new opportunities for the long-term, high-quality monitoring of brain signals and for the use of controlled feedback therapies to treat brain diseases. Additionally, we highlight device applications that have shown promise in clinical studies, discuss issues associated with the clinical translation of soft bioelectronics, and end with future directions for the improved design of soft bioelectronics. A key issue in conventional bioelectronics is a mechanical mismatch,16Feron K. Lim R. Sherwood C. Keynes A. Brichta A. Dastoor P.C. Organic bioelectronics: materials and biocompatibility.Int. J. Mol. Sci. 2018; 19: 2382Crossref Scopus (22) Google Scholar,17Someya T. Bao Z. Malliaras G.G. The rise of plastic bioelectronics.Nature. 2016; 540: 379-385Crossref PubMed Scopus (540) Google Scholar which mainly derives from two important contributors of a device, namely its material properties and its geometry, which are interdependent. Young's modulus, which defines how a material will strain (that is, deform) under stress (as measured by force/unit area), differs by several orders of magnitude between conventional rigid bioelectronic devices (which have moduli in the order of GPa) and soft brain tissues (which have moduli in the order of kPa).18Bettinger C.J. Recent advances in materials and flexible electronics for peripheral nerve interfaces.Bioelectron. Med. 2018; 4: 6Crossref PubMed Google Scholar The overall size and design of electrodes also affect the bending stiffness, which is proportional to Young's modulus, the width of the probe, and the third power of thickness of the probe.19Xie C. Liu J. Fu T.M. Dai X. Zhou W. Lieber C.M. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes.Nat. Mater. 2015; 14: 1286-1292Crossref PubMed Scopus (181) Google Scholar As a result, rigid implanted devices, conventionally made of tungsten wires or silicon probes, can cause chronic stressors, which over a long period of time can cause significant damage to brain tissue as well as to the implant itself, generating functional failures in both (Figure 1A). In contrast, soft bioelectronics can better endure the same stressors (Figure 1B). Device implantation, such as for intracortical recording, can also cause damage to an implant and the surrounding tissue during the process of implantation (for example through scarring, bleeding, and inflammatory reactions). There are also long-term effects, such as the isolation of a bioelectronic device due to fibrous scar formation and subsequent chronic inflammation, which can include protein attachment, astrocyte recruitment, and fibrosis around implanted electrodes. The formation of these fibrous tissues increases the electrical impedance at the tissue-electrode interface and the degradation of measured electrical signals (Figure 1C). The geometry and size of an implant also determine the extent of damage. For example, larger bioelectrodes exhibit higher mechanical mismatch and cause greater mechanical damage.20Singh S. Lo M.-C. Damodaran V. Kaplan H. Kohn J. Zahn J. Shreiber D. Modeling the insertion mechanics of flexible neural probes coated with sacrificial polymers for optimizing probe design.Sensors (Basel). 2016; 16: 330Crossref Scopus (0) Google Scholar The vigorous micromotions of the brain also cause chronic stress on the tissue.21Pan L. Wang F. Cheng Y. Leow W.R. Zhang Y.W. Wang M. Cai P. Ji B. Li D. Chen X. A supertough electro-tendon based on spider silk composites.Nat. Commun. 2020; 11: 1332Crossref PubMed Scopus (5) Google Scholar,22Prodanov D. Delbeke J. Mechanical and biological interactions of implants with the brain and their impact on implant design.Front. Neurosci. 2016; 10: 11Crossref PubMed Scopus (45) Google Scholar Moreover, the mechanical stress on the implanted electrode can lead to a failure of the encapsulation layer, causing electrical current to leak to non-targeted brain regions23Nolta N.F. Ghelich P. Han M. Recessed traces for planarized passivation of chronic neural microelectrodes.in: Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019: 5125-5128Crossref Scopus (1) Google Scholar and making the electrodes vulnerable to corrosion. In the case of less invasive, skin-mounted EEG measurement devices, the main challenge is to maintain high-quality, long-term conformal contact between the bioelectronics and the elastic, wrinkled skin.24Lee S.M. Kim J.H. Park C. Hwang J.Y. Hong J.S. Lee K.H. Lee S.H. Self-adhesive and capacitive carbon nanotube-based electrode to record electroencephalograph signals from the hairy scalp.IEEE Trans. Biomed. Eng. 2016; 63: 138-147Crossref PubMed Scopus (14) Google Scholar,25Zou Y. Dehzangi O. Nathan V. Jafari R. Automatic removal of EEG artifacts using electrode-scalp impedance.in: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014: 2054-2058Crossref Scopus (3) Google Scholar Due to the stiffness of the constituent materials, conventional electrodes cannot perfectly follow the microscopic curvature of the wrinkled skin. Additionally, repetitive stretching of elastic skin often causes electrodes to delaminate from it. Such non-conformal contact with the skin surface can abruptly increase impedance and dramatically decrease the signal-to-noise ratio (SNR). Furthermore, the long-term attachment of rigid electrodes to the skin leads to skin irritation, necessitating the use of softer materials.26Drees C. Makic M.B. Case K. Mancuso M.P. Hill A. Walczak P. Limon S. Biesecker K. Frey L. Skin irritation during video-EEG monitoring.Neurodiagn. J. 2016; 56: 139-150Crossref PubMed Scopus (4) Google Scholar,27Lau-Zhu A. Lau M.P.H. McLoughlin G. Mobile EEG in research on neurodevelopmental disorders: opportunities and challenges.Dev. Cogn. Neurosci. 2019; 36: 100635Crossref PubMed Scopus (19) Google Scholar Another key issue is a chemical mismatch caused by differences between the chemical composition of brain tissue and the inorganic materials used in conventional bioelectronics. While brain tissue contains a large amount of water and is ion-rich, the inorganic materials used in bioelectronic devices can be hydrophobic in nature and can have low fluid permeability, causing low cellular affinity, which impedes cell attachment and cell-electrode interactions.28Bhattarai S.R. Bhattarai N. Viswanathamurthi P. Yi H.K. Hwang P.H. Kim H.Y. Hydrophilic nanofibrous structure of polylactide; fabrication and cell affinity.J. Biomed. Mater. Res. A. 2006; 78: 247-257Crossref PubMed Scopus (99) Google Scholar The interface between a device's electrodes and brain tissue is called the neural interface, a solid electrode interface with an electrolytic solution where charge transfers occur between the ions in the fluid and the electrons in the electrode (Figure 1D). As with all such electrode/electrolyte systems, discontinuities in the implant/tissue interface distort the measured electrical signals. The degree of this distortion depends on differences in the junction potential of two interfacing partners (i.e., the extracellular fluid and electrode materials). Such discontinuity at the interface apparently increases the interfacial impedance, which decreases the SNR.29Fang Y. Li X. Fang Y. Organic bioelectronics for neural interfaces.J. Mater. Chem. C. 2015; 3: 6424-6430Crossref Google Scholar In addition to a poor SNR and distorted electrical recordings, current leakage from a damaged rigid electrode (Figure 1A) can result in undesirable electrochemical reactions, such as the electrolysis of water, the oxidation of materials, and the reduction of ions, which can also damage brain tissue.30Abidian M.R. Martin D.C. Experimental and theoretical characterization of implantable neural microelectrodes modified with conducting polymer nanotubes.Biomaterials. 2008; 29: 1273-1283Crossref PubMed Scopus (0) Google Scholar As we discuss in the remainder of this review, novel technological approaches are being developed that aim to minimize the aforementioned mechanical, chemical, and electrical differences between the conventional bioelectronic devices and brain tissue. These approaches are developing soft bioelectronic devices—based on unconventional materials, new fabrication processes, and device designs—that aim to be biocompatible, reliable, and stable over long time periods in vivo. In this section, we discuss efforts to develop bioelectronics that minimize the mechanical, chemical, and electrical differences between tissues and devices through the use of unconventional soft materials (Figure 2A), the characteristics of which are summarized in Table 1.Table 1General Characteristics of Representative Unconventional MaterialsMechanical PropertyChemical PropertyElectrical PropertyFabrication ResolutionReferencesSoftnessaThe value of bending stiffness may vary by thickness.ThicknessBiocompatibilityLifetimeImpedanceConductivity(Ω, @1 KHz)(S/cm)Metal layerAurigidvery thincompatiblelonghighvery highhigh10Kim T.-I. McCall J.G. Jung Y.H. Huang X. Siuda E.R. Li Y. Song J. Song Y.M. Pao H.A. Kim R.H. et al.Injectable, cellular-scale optoelectronics with applications for wireless optogenetics.Science. 2013; 340: 211-216Crossref PubMed Scopus (673) Google Scholar,31Liu J. Fu T.M. Cheng Z. Hong G. Zhou T. Jin L. Duvvuri M. Jiang Z. Kruskal P. Xie C. et al.Syringe-injectable electronics.Nat. Nanotechnol. 2015; 10: 629-635Crossref PubMed Scopus (298) Google Scholar,19Xie C. Liu J. Fu T.M. Dai X. Zhou W. Lieber C.M. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes.Nat. Mater. 2015; 14: 1286-1292Crossref PubMed Scopus (181) Google Scholar∼79 GPa∼1 MΩ411,000Ptrigidvery thincompatiblelonglowhighhigh36Chapman C.A.R. Aristovich K. Donega M. Fjordbakk C.T. Stathopoulou T.R. Viscasillas J. et al.Electrode fabrication and interface optimization for imaging of evoked peripheral nervous system activity with electrical impedance tomography (EIT).J. Neural Eng. 2019; 16: 016001Crossref PubMed Scopus (6) Google Scholar∼168 GPa∼400 Ω94,300IrOxrigidvery thincompatiblelonglowvery highhigh36Chapman C.A.R. Aristovich K. Donega M. Fjordbakk C.T. Stathopoulou T.R. Viscasillas J. et al.Electrode fabrication and interface optimization for imaging of evoked peripheral nervous system activity with electrical impedance tomography (EIT).J. Neural Eng. 2019; 16: 016001Crossref PubMed Scopus (6) Google Scholar,37Ji B. Guo Z. Wang M. Yang B. Wang X. Li W. et al.Flexible polyimide-based hybrid opto-electric neural interface with 16 channels of micro-LEDs and electrodes.Microsyst Nanoeng. 2018; 4https://doi.org/10.1038/s41378-018-0027-0Crossref PubMed Scopus (12) Google Scholar∼530 GPa∼350 Ω213,000Conductive polymerPEDOT:PSSmoderatethinmoderatemoderatelowmoderatemoderate36Chapman C.A.R. Aristovich K. Donega M. Fjordbakk C.T. Stathopoulou T.R. Viscasillas J. et al.Electrode fabrication and interface optimization for imaging of evoked peripheral nervous system activity with electrical impedance tomography (EIT).J. Neural Eng. 2019; 16: 016001Crossref PubMed Scopus (6) Google Scholar,38Ganji M. Tanaka A. Gilja V. Halgren E. Dayeh S.A. Scaling effects on the electrochemical stimulation performance of Au, Pt, and PEDOT:PSS electrocorticography arrays.Adv. Funct. Mater. 2017; 27: 1703019Crossref Scopus (16) Google Scholar,39Ganji M. Kaestner E. Hermiz J. Rogers N. Tanaka A. Cleary D. Lee S.H. Snider J. Halgren M. Cosgrove G.R. et al.Development and translation of PEDOT:PSS microelectrodes for intraoperative monitoring.Adv. Funct. Mater. 2018; 28: 1700232Crossref Scopus (32) Google Scholar,40Lang U. Naujoks N. Dual J. Mechanical characterization of PEDOT:PSS thin films.Synth. Met. 2009; 159: 473-479Crossref Scopus (196) Google Scholar∼2 GPa∼300 Ω10–1,000PPymoderatethinmoderatemoderatelowmoderatemoderate41Alegret N. Dominguez-Alfaro A. González-Domínguez J.M. Arnaiz B. Cossío U. Bosi S. et al.Three-Dimensional Conductive Scaffolds as Neural Prostheses Based on Carbon Nanotubes and Polypyrrole.ACS Appl. Mater. Interfaces. 2018; 10: 43904-43914Crossref PubMed Scopus (10) Google Scholar,42Foroughi J., Ghorbani S.R., Peleckis G., Spinks G.M., Wallace G.G., Wang X.L., et al. The mechanical and the electrical properties of conducting polypyrrole fibers. J. Appl. Phys. 107, 103712.Google Scholar,43Kojabad Z.D. Shojaosadati S.A. Chemical Synthesis of Polypyrrole Nanotubes for Neural Microelectrodes.Procedia Mater Sci. 2015; 11: 147-151Crossref Google Scholar,44Sakurai T. Toyoshima S. Kitazume H. Masuda S. Kato H. Akimoto K. Influence of gap states on electrical properties at interface between bathocuproine and various types of metals.J. Appl. Phys. 2010; 107: 043707Crossref Scopus (44) Google Scholar∼600 MPa∼100 Ω2–100Hydrogelpurevery softmoderatecompatibleshorthighvery lowlow45Rivnay J. Wang H. Fenno L. Deisseroth K. Malliaras G.G. Next-generation probes, particles, and proteins for neural interfacing.Sci. Adv. 2017; 3: e1601649Crossref PubMed Scopus (0) Google Scholar,46Yuk H. Lu B. Zhao X. Hydrogel bioelectronics.Chem. Soc. Rev. 2019; 48: 1642-1667Crossref PubMed Google Scholar,47Lu Y. Wang D. Li T. Zhao X. Cao Y. Yang H. et al.Poly(vinyl alcohol)/poly(acrylic acid) hydrogel coatings for improving electrode-neural tissue interface.Biomaterials. 2009; 30: 4143-4151Crossref PubMed Scopus (0) Google Scholar∼100 kPa10 MΩ0.0001conducting polymer fillersbThe value may vary by filler materials.very softmoderatecompatibleshortmoderatemoderatelow48Kleber C. Bruns M. Lienkamp K. Rühe J. Asplund M. An interpenetrating, microstructurable and covalently attached conducting polymer hydrogel for neural interfaces.Acta Biomater. 2017; 58: 365-375Crossref PubMed Scopus (28) Google Scholar,49Liu Y. Liu J. Chen S. Lei T. Kim Y. Niu S. Wang H. Wang X. Foudeh A.M. Tok J.B.H. et al.Soft and elastic hydrogel-based microelectronics for localized low-voltage neuromodulation.Nat. Biomed. Eng. 2019; 3: 58-68Crossref PubMed Scopus (91) Google Scholar,50Stavrinidou E. Leleux P. Rajaona H. Khodagholy D. Rivnay J. Lindau M. Sanaur S. Malliaras G.G. Direct measurement of ion mobility in a conducting polymer.Adv. Mater. 2013; 25: 4488-4493Crossref PubMed Scopus (148) Google Scholar∼32 kPa∼15 kΩ∼47.4nanomaterial fillersbThe value may vary by filler materials.very softmoderatecompatibleshortmoderatelowlow34Ahn Y. Lee H. Lee D. Lee Y. Highly conductive and flexible silver nanowire-based microelectrodes on biocompatible hydrogel.ACS Appl. Mater. Interfaces. 2014; 6: 18401-18407Crossref PubMed Scopus (70) Google Scholar,51Cha C. Shin S.R. Annabi N. Dokmeci M.R. Khademhosseini A. Carbon-based nanomaterials: multifunctional materials for biomedical engineering.ACS Nano. 2013; 7: 2891-2897Crossref PubMed Scopus (396) Google Scholar,52Gaharwar A.K. Peppas N.A. Khademhosseini A. Nanocomposite hydrogels for biomedical applications.Biotechnol. Bioeng. 2014; 111: 441-453Crossref PubMed Scopus (533) Google Scholar,49Liu Y. Liu J. Chen S. Lei T. Kim Y. Niu S. Wang H. Wang X. Foudeh A.M. Tok J.B.H. et al.Soft and elastic hydrogel-based microelectronics for localized low-voltage neuromodulation.Nat. Biomed. Eng. 2019; 3: 58-68Crossref PubMed Scopus (91) Google Scholar,53Rafieian S. Mirzadeh H. Mahdavi H. Masoumi M.E. A review on nanocomposite hydrogels and their biomedical applications.IEEE J. Sel. Top. Quan. Electron. 2019; 26: 154-174Google Scholar, 54Xu C. Ma B. Yuan S. Zhao C. Liu H. High-Resolution Patterning of Liquid Metal on Hydrogel for Flexible, Stretchable, and Self-Healing Electronics.Adv. Electron. Mater. 2020; 6: 1900721Crossref Scopus (7) Google Scholar,55Zhou Y. Wan C. Yang Y. Yang H. Wang S. Dai Z. et al.Highly Stretchable, Elastic, and Ionic Conductive Hydrogel for Artificial Soft Electronics.Adv. Funct. Mater. 2019; 29: 1806220Crossref Scopus (120) Google Scholar∼100 kPa∼1.5 kΩ∼0.1Elastomeric nanocompositebThe value may vary by filler materials.softmoderatemoderatemoderatemoderatehighlow56Choi S. Han S.I. Jung D. Hwang H.J. Lim C. Bae S. Park O.K. Tschabrunn C.M. Lee M. Bae S.Y. et al.Highly conductive, stretchable and biocompatible Ag-Au core-sheath nanowire composite for wearable and implantable bioelectronics.Nat. Nanotechnol. 2018; 13: 1048-1056Crossref PubMed Scopus (202) Google Scholar,57Joo H. Jung D. Sunwoo S.H. Koo J.H. Kim D.H. Material design and fabrication strategies for stretchable metallic nanocomposites.Small. 2020; 16: 1906270Crossref Scopus (6) Google Scholar,58Parameswaran R. Koehler K. Rotenberg M.Y. Burke M.J. Kim J. Jeong K.Y. Hissa B. Paul M.D. Moreno K. Sarma N. et al.Optical stimulation of cardiac cells with a polymer-supported silicon nanowire matrix.Proc. Natl. Acad. Sci. U S A. 2019; 116: 413-421Crossref PubMed Scopus (8) Google Scholar,35Park J. Choi S. Janardhan A.H. Lee S.Y. Raut S. Soares J. Shin K. Yang S. Lee C. Kang K.W. et al.Electromechanical cardioplasty using a wrapped elasto-conductive epicardial mesh.Sci. Transl. Med. 2016; 8: 344ra86Crossref PubMed Scopus (99) Google Scholar,59Sunwoo S.H. Han S.I. Kang H. Cho Y.S. Jung D. Lim C. Lim C. Cha M.J. Lee S.P. Hyeon T. et al.Stretchable low-impedance nanocomposite comprised of Ag-Au core-shell nanowires and Pt black for epicardial recording and stimulation.Adv. Mater. Technol. 2020; 5: 1900768Crossref Scopus (5) Google Scholar∼4 MPa∼1 kΩ∼72,000Conventional materialmetal/siliconrigidthickcompatiblemoderatehighvery highhigh∼185 GPa∼1 MΩ411,000The detailed value and properties may vary depending on the material type and thickness.a The value of bending stiffness may vary by thickness.b The value may vary by filler materials. Open table in a new tab The detailed value and properties may vary depending on the material type and thickness. The mechanical stiffness of a material is highly dependent on its geometry; a conventional material processed into an ultrathin form (of <10 μm)60Kim D.H. Rogers J.A. Bend, buckle, and fold: mechanical engineering with nanomembranes.ACS Nano. 2009; 3: 498-501Crossref PubMed Scopus (39) Google Scholar,61Wang S. Song J. Kim D.H. Huang Y. Rogers J.A. Local versus global buckling of thin films on elastomeric substrates.Appl. Phys. Lett. 2008; 93: 023126Crossref Scopus (54) Google Scholar will show a dramatic decrease in its stiffness. Importantly, reducing a material's thickness decreases its flexural rigidity without losing the original electrical properties of its bulk state.62Choi C. Lee Y. Cho K.W. Koo J.H. Kim D.H. Wearable and implantable soft bioelectronics using two-dimensional materials.Acc. Chem. Res. 2019; 52: 73-81Crossref PubMed Scopus (39) Google Scholar,63Kim D.H. Lu N. Ma R. Kim Y.S. Kim R.H. Wang S. Wu J. Won S.M. Tao H. Islam A. et al.Epidermal electronics.Science. 2011; 333: 838-843Crossref PubMed Scopus (2588) Google Scholar,35Park J. Choi S. Janardhan A.H. Lee S.Y. Raut S. Soares J. Shin K. Yang S. Lee C. Kang K.W. et al.Electromechanical cardioplasty using a wrapped elasto-conductive epicardial mesh.Sci. Transl. Med. 2016; 8: 344ra86Crossref PubMed Scopus (99) Google Scholar Ultrathin materials thus become soft, exhibiting much smaller mechanical differences from brain tissue in terms of bending stiffness,64Kim D.H. Viventi J. Amsden J.J. Xiao J. Vigeland L. Kim Y.S. Blanco J.A. Panilaitis B. Frechette E.S. Contreras D. et al.Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.Nat. Mater. 2010; 9: 511-517Crossref PubMed Scopus (985) Google Scholar while still serving as an electronic device component with the properties of a conventional bulk-state material. As such, devices based on ultrathin materials can make soft, conformal contact with brain tissue, solving the problem of mechanical mismatch. Biocompatible metals, such as gold,64Kim D.H. Viventi J. Amsden J.J. Xiao J. Vigeland L. Kim Y.S. Blanco J.A. Panilaitis B. Frechette E.S. Contreras D. et al.Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.Nat. Mater. 2010; 9: 511-517Crossref PubMed Scopus (985) Google Scholar platinum,65Petit-Pierre G. Bertsch A. Renaud P. Neural probe combining microelectrodes and a droplet-based microdialysis collection system for high temporal resolution sampling.Lab Chip. 2016; 16: 917-924Crossref PubMed Google Scholar iridium,66Lee H.J. Son Y. Kim J. Lee C.J. Yoon E.S. Cho I.J. A multichannel neural probe with embedded microfluidic channels for simultaneous in vivo neural recording and drug delivery.Lab Chip. 2015; 15: 1590-1597Crossref PubMed Google Scholar and their alloys, placed on flexible polymer substrates such as polyimide, parylene, and epoxy, have been used to generate flexible, ultrathin bioelectronics. The geometry of ultrathin devices can be designed as a mesh structure to maximize the effect of ultrathin materials on the deformability of the device.10Kim