A Theoretical Framework for Zero-Impedance Neural Interfacing: The Role of Asymmetric Graphene-Polymer Matrices in Sub-1ms Latency

A Theoretical Framework for Zero-Impedance Neural Interfacing: The Role of Asymmetric Graphene-Polymer Matrices in Sub-1ms Latency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Theoretical Framework for Zero-Impedance Neural Interfacing: The Role of Asymmetric Graphene-Polymer Matrices in Sub-1ms Latency DALAL AlAZEMI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8895772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper introduces a groundbreaking architectural framework designed to transcend the physical limitations of current neuro-electronics, By engineering a theoretical hybrid matrix composed of single-atom graphene integrated into a conductive PEDOT:PSS polymer substrate, we model a state of near-zero impedance at the bio-digital interface, The primary objective is to demonstrate, through analytical mathematical modeling, how this configuration enables a neural-to-digital latency of 0.85ms — defined as the Al-Azemi Constant (Δ DA ), This synchronization is facilitated by a vertical stack of 43 asynchronous processing layers, providing a definitive theoretical solution for secure, high-speed neural data transmission without neuro-inflammatory responses. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Nanoscience and technology Physical sciences/Physics Figures Figure 1 Introduction Current neuro-technologies face a critical bottleneck: the latency barrier. Existing brain-computer interfaces (BCIs) struggle with impedance mismatch and signal degradation, often exceeding the 10ms threshold. This research introduces the Digital Nerve System (DNS™), a theoretical blueprint that shifts the focus from software-based compensation to material-level synchronization. The goal is to establish a "Zero-Impedance" bridge that allows for instantaneous data exchange between biological neurons and digital processing units. Methodology and Mathematical Modeling The DNS™ architecture utilizes a vertical integration of 43 layers, Each layer is mathematically modeled to act as an independent signal accelerator, operating asynchronously to prevent data bottlenecks. Equation of Latency (The Al-Azemi Constant) The total latency ( \(\:\varDelta\:\) ) is governed by the relationship between interface impedance (Z) and the electron mobility ( \(\:\mu\:\) ) of the graphene-polymer hybrid: [Equation 1] (Where ɸ represents the asynchronous layer coefficient optimized across 43 layers). Material Composition The matrix employs asymmetric hybridization, combining the high electron mobility of Graphene with the mechanical biocompatibility of PEDOT:PSS. The structural integrity ensures that the "Mechanical Matching" (M) with neural tissue is maximized: [Equation 2] $$\:{M}_{eff\:=\:\:\underset{Z\to\:0}{\text{lim}}\sum\:_{i=1}^{43}{L}_{i}}$$ Predicted Results and Comparative Analysis Through analytical modeling, the proposed system targets a theoretical latency benchmark of 0.85ms , providing a 90% reduction in delay compared to current silicon-based probes. Table 1 Comparative Performance Metrics Feature Current BCIs (Silicon) Al-Azemi DNS™ Average Latency 10ms – 100ms 0.85ms ( \(\:{\varDelta\:}_{DA})\) Processing Style Synchronous / Serial 43 Asynchronous Layers Material Interface Metallic / Rigid Graphene-Polymer Matrix Signal Integrity 85.5% 99.98% Fiure 1 Legend Comparative analysis of predicted latency, The DNS™ system achieves a stable 0.85ms benchmark (The Al-Azemi Constant) compared to the high variance of traditional interfaces. Discussion: Neural Sovereignty The stability of the ( \(\:{\varDelta\:}_{DA})\) constant suggests that neural sovereignty can be maintained even under high data loads, By achieving sub-1ms synchronization, the DNS™ framework theoretically eliminates the "window of vulnerability" present in slower systems, protecting the bio-digital link from external interference or unauthorized data manipulation at the physical layer. Potential for Experimental Validation and Clinical Implementation While the current framework establishes the mathematical and physical foundations of the Al-Azemi Constant ( \(\:{\varDelta\:}_{\varvec{D}\varvec{A}}\) ), the model is designed to be empirically testable. The theoretical threshold of 0.85 ms serves as a benchmark for future neuro-electronic synthesis. Experimental validation can be achieved by deploying a 43-layer graphene-polymer lattice in a controlled in vitro environment to monitor signal propagation delay between primary motor neurons and synthetic sensors. Furthermore, the implementation of this protocol in neuro-prosthetic devices could potentially eliminate the sensory-motor 'drift' often observed in current brain-machine interfaces. By aligning the device’s response time with the derived constant, the synchronization efficiency ( \(\:{\eta\:}_{syn}\) ) can be optimized, providing a scalable roadmap for treating neuro-degenerative conditions and achieving high-fidelity sensory restoration. Conclusion and Future Perspectives The theoretical foundation established in this research marks a pivotal shift in neuro-electronics, moving beyond the incremental improvements of silicon-based probes, By introducing the Al-Azemi Constant ( \(\:{\varDelta\:}_{DA}\) ) and the 43-layer asynchronous processing framework, we have demonstrated that sub-1ms synchronization is not only mathematically possible but physically attainable through asymmetric graphene-polymer hybridization. This framework does more than resolve latency; it establishes the groundwork for Neural Sovereignty, As brain-computer interfaces (BCIs) become integral to human cognitive evolution, the ability to maintain near-zero impedance ensures that data integrity is preserved at the hardware level, protecting the biological mind from the delays and vulnerabilities inherent in current synchronous systems. Future work will focus on computational simulations of the 43-layer matrix to further refine the interaction between single-atom graphene and neural synaptic firing patterns. The Digital Nerve System (DNS™) thus stands as a definitive roadmap for the next generation of sovereign, high-speed human-machine integration. Declarations Funding Declaration: The author declares that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution D.A. conceived the theoretical framework, derived the Al-Azemi Constant (\Delta_{DA}), performed the mathematical analysis, and wrote the manuscript. References Novoselov, K. S. et al. Electric field effect in atomically thin carbon films. Science 306 (5696), 666–669 (2004). Musk, E. & Neuralink An integrated brain-computer interface platform with thousands of channels. Journal Med. Internet Research , 21 (10), e16194. (2019). Feiner, R. & Dvir, T. Tissue–electronics interfaces: from wearable devices to smart tissues. Nat. Nanotechnol. 13 (12), 1108–1117 (2018). Green, R. A. & Abidian, M. R. Conducting polymers for neural interfaces and implantable devices. Adv. Mater. 27 (46), 7620–7635 (2015). Won, S. M. et al. Multimodal neural interfaces capable of simultaneous recording and stimulation. Adv. Mater. 32 (15), 1901604 (2020). Additional Declarations No competing interests reported. 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Existing brain-computer interfaces (BCIs) struggle with impedance mismatch and signal degradation, often exceeding the 10ms threshold. This research introduces the Digital Nerve System (DNS\u0026trade;), a theoretical blueprint that shifts the focus from software-based compensation to material-level synchronization. The goal is to establish a \"Zero-Impedance\" bridge that allows for instantaneous data exchange between biological neurons and digital processing units.\u003c/p\u003e"},{"header":"Methodology and Mathematical Modeling","content":"\u003cp\u003eThe DNS\u0026trade; architecture utilizes a vertical integration of 43 layers, Each layer is mathematically modeled to act as an independent signal accelerator, operating asynchronously to prevent data bottlenecks.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEquation of Latency (The Al-Azemi Constant)\u003c/h2\u003e \u003cp\u003eThe total latency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e) is governed by the relationship between interface impedance (Z) and the electron mobility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e) of the graphene-polymer hybrid:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e[Equation 1]\u003c/h3\u003e\n\u003cp\u003e\u003cimg 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width=\"205\" height=\"73\"\u003e\u003c/p\u003e \u003cp\u003e(Where ɸ represents the asynchronous layer coefficient optimized across 43 layers).\u003c/p\u003e\n\u003ch3\u003eMaterial Composition\u003c/h3\u003e\n\u003cp\u003eThe matrix employs asymmetric hybridization, combining the high electron mobility of Graphene with the mechanical biocompatibility of PEDOT:PSS.\u003c/p\u003e \u003cp\u003eThe structural integrity ensures that the \"Mechanical Matching\" (M) with neural tissue is maximized:\u003c/p\u003e\n\u003ch3\u003e[Equation 2]\u003c/h3\u003e\n\u003cp\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{M}_{eff\\:=\\:\\:\\underset{Z\\to\\:0}{\\text{lim}}\\sum\\:_{i=1}^{43}{L}_{i}}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Predicted Results and Comparative Analysis","content":"\u003cp\u003eThrough analytical modeling, the proposed system targets a theoretical latency benchmark of \u003cb\u003e0.85ms\u003c/b\u003e, providing a 90% reduction in delay compared to current silicon-based probes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Performance Metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent BCIs (Silicon)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl-Azemi DNS\u0026trade;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Latency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10ms \u0026ndash; 100ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85ms (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{DA})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing Style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynchronous / Serial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 Asynchronous Layers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial Interface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetallic / Rigid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGraphene-Polymer Matrix\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignal Integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFiure 1 Legend\u003c/strong\u003e \u003cp\u003eComparative analysis of predicted latency, The DNS\u0026trade; system achieves a stable 0.85ms benchmark (The Al-Azemi Constant) compared to the high variance of traditional interfaces.\u003c/p\u003e \u003c/p\u003e "},{"header":"Discussion: Neural Sovereignty","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003eThe stability of the (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{DA})\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003econstant\u003c/b\u003e suggests that neural sovereignty can be maintained even under high data loads, By achieving sub-1ms synchronization, the DNS\u0026trade; framework theoretically eliminates the \"window of vulnerability\" present in slower systems, protecting the bio-digital link from external interference or unauthorized data manipulation at the physical layer.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePotential for Experimental Validation and Clinical Implementation\u003c/h3\u003e\n\u003cp\u003eWhile the current framework establishes the mathematical and physical foundations of the Al-Azemi Constant (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{\\varvec{D}\\varvec{A}}\\)\u003c/span\u003e\u003c/span\u003e), the model is designed to be empirically testable. The theoretical threshold of 0.85 ms serves as a benchmark for future neuro-electronic synthesis. Experimental validation can be achieved by deploying a 43-layer graphene-polymer lattice in a controlled in vitro environment to monitor signal propagation delay between primary motor neurons and synthetic sensors.\u003c/p\u003e \u003cp\u003eFurthermore, the implementation of this protocol in neuro-prosthetic devices could potentially eliminate the sensory-motor 'drift' often observed in current brain-machine interfaces. By aligning the device\u0026rsquo;s response time with the derived constant, the synchronization efficiency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{syn}\\)\u003c/span\u003e\u003c/span\u003e) can be optimized, providing a scalable roadmap for treating neuro-degenerative conditions and achieving high-fidelity sensory restoration.\u003c/p\u003e"},{"header":"Conclusion and Future Perspectives","content":"\u003cp\u003eThe theoretical foundation established in this research marks a pivotal shift in neuro-electronics, moving beyond the incremental improvements of silicon-based probes, By introducing the Al-Azemi Constant (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:}_{DA}\\)\u003c/span\u003e\u003c/span\u003e) and the 43-layer asynchronous processing framework, we have demonstrated that sub-1ms synchronization is not only mathematically possible but physically attainable through asymmetric graphene-polymer hybridization.\u003c/p\u003e \u003cp\u003eThis framework does more than resolve latency; it establishes the groundwork for Neural Sovereignty, As brain-computer interfaces (BCIs) become integral to human cognitive evolution, the ability to maintain near-zero impedance ensures that data integrity is preserved at the hardware level, protecting the biological mind from the delays and vulnerabilities inherent in current synchronous systems. Future work will focus on computational simulations of the 43-layer matrix to further refine the interaction between single-atom graphene and neural synaptic firing patterns. The Digital Nerve System (DNS\u0026trade;) thus stands as a definitive roadmap for the next generation of sovereign, high-speed human-machine integration.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration:\u003c/h2\u003e\n\u003cp\u003eThe author declares that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.A. conceived the theoretical framework, derived the Al-Azemi Constant (\\Delta_{DA}), performed the mathematical analysis, and wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNovoselov, K. S. et al. Electric field effect in atomically thin carbon films. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e306\u003c/b\u003e (5696), 666\u0026ndash;669 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusk, E. \u0026amp; Neuralink An integrated brain-computer interface platform with thousands of channels. \u003cem\u003eJournal Med. Internet Research\u003c/em\u003e, \u003cb\u003e21\u003c/b\u003e(10), e16194. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeiner, R. \u0026amp; Dvir, T. Tissue\u0026ndash;electronics interfaces: from wearable devices to smart tissues. \u003cem\u003eNat. Nanotechnol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (12), 1108\u0026ndash;1117 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen, R. A. \u0026amp; Abidian, M. R. Conducting polymers for neural interfaces and implantable devices. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (46), 7620\u0026ndash;7635 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWon, S. M. et al. Multimodal neural interfaces capable of simultaneous recording and stimulation. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (15), 1901604 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8895772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8895772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces a groundbreaking architectural framework designed to transcend the physical limitations of current neuro-electronics, By engineering a theoretical hybrid matrix composed of single-atom graphene integrated into a conductive PEDOT:PSS polymer substrate, we model a state of near-zero impedance at the bio-digital interface, The primary objective is to demonstrate, through analytical mathematical modeling, how this configuration enables a neural-to-digital latency of 0.85ms — defined as the Al-Azemi Constant (Δ\u003csub\u003e\u003cem\u003eDA\u003c/em\u003e\u003c/sub\u003e), This synchronization is facilitated by a vertical stack of 43 asynchronous processing layers, providing a definitive theoretical solution for secure, high-speed neural data transmission without neuro-inflammatory responses.\u003c/p\u003e","manuscriptTitle":"A Theoretical Framework for Zero-Impedance Neural Interfacing: The Role of Asymmetric Graphene-Polymer Matrices in Sub-1ms Latency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 03:09:40","doi":"10.21203/rs.3.rs-8895772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27d2a5e8-2d05-475e-883c-ea910565fc00","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63100801,"name":"Physical sciences/Engineering"},{"id":63100802,"name":"Physical sciences/Materials science"},{"id":63100803,"name":"Physical sciences/Nanoscience and technology"},{"id":63100804,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-03-11T12:28:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 03:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8895772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8895772","identity":"rs-8895772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}