Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms
This computational study investigates dynamic self-healing processes in nanomaterials driven by neuronal spike activity. We developed a multiscale simulation framework that integrates neuronal dynamics, quantum mechanical effects, and material science principles. Our model incorporates a time-depend...
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MDPI AG
2024-12-01
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| author | Jongho Seol Jongyeop Kim Abhilash Kancharla |
| author_facet | Jongho Seol Jongyeop Kim Abhilash Kancharla |
| author_sort | Jongho Seol |
| collection | DOAJ |
| description | This computational study investigates dynamic self-healing processes in nanomaterials driven by neuronal spike activity. We developed a multiscale simulation framework that integrates neuronal dynamics, quantum mechanical effects, and material science principles. Our model incorporates a time-dependent neuron spike voltage equation coupled with a nanomaterial health update function, including quantum probability terms, to capture nanoscale effects. We employ reliability engineering concepts to assess system performance. Simulations reveal that neuronal spike patterns significantly influence self-healing dynamics, exhibiting non-linear behavior with quantum effects crucial to healing efficiency. Statistical analysis demonstrates a strong correlation between spike frequency and healing rate, identifying an optimal range for maximum recovery. Integrating quantum probabilities yields more accurate nanoscale behavior predictions than classical approaches alone. This study provides a foundation for understanding and optimizing neuronal spike-induced recovery in nanomaterials with potential applications in neural interfaces, intelligent materials, and biomedical devices. |
| format | Article |
| id | doaj-art-2799558c40504662bfe72b4c26843d2b |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-2799558c40504662bfe72b4c26843d2b2025-08-20T02:53:26ZengMDPI AGInformation2078-24892024-12-01151279410.3390/info15120794Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike AlgorithmsJongho Seol0Jongyeop Kim1Abhilash Kancharla2Department of Computer Science, Middle Georgia State University, Warner Robins, GA 31093, USADepartment of Information Technology, Georgia Southern University, Statesboro, GA 30458, USADepartment of Computer Science, University of Tampa, Tampa, FL 33606, USAThis computational study investigates dynamic self-healing processes in nanomaterials driven by neuronal spike activity. We developed a multiscale simulation framework that integrates neuronal dynamics, quantum mechanical effects, and material science principles. Our model incorporates a time-dependent neuron spike voltage equation coupled with a nanomaterial health update function, including quantum probability terms, to capture nanoscale effects. We employ reliability engineering concepts to assess system performance. Simulations reveal that neuronal spike patterns significantly influence self-healing dynamics, exhibiting non-linear behavior with quantum effects crucial to healing efficiency. Statistical analysis demonstrates a strong correlation between spike frequency and healing rate, identifying an optimal range for maximum recovery. Integrating quantum probabilities yields more accurate nanoscale behavior predictions than classical approaches alone. This study provides a foundation for understanding and optimizing neuronal spike-induced recovery in nanomaterials with potential applications in neural interfaces, intelligent materials, and biomedical devices.https://www.mdpi.com/2078-2489/15/12/794nanomaterialsself-healingneuronal spikescomputational modelingquantum effectsdynamic recovery |
| spellingShingle | Jongho Seol Jongyeop Kim Abhilash Kancharla Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms Information nanomaterials self-healing neuronal spikes computational modeling quantum effects dynamic recovery |
| title | Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms |
| title_full | Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms |
| title_fullStr | Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms |
| title_full_unstemmed | Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms |
| title_short | Computational Analysis of Self-Healing in Nanomaterials Using Neural Spike Algorithms |
| title_sort | computational analysis of self healing in nanomaterials using neural spike algorithms |
| topic | nanomaterials self-healing neuronal spikes computational modeling quantum effects dynamic recovery |
| url | https://www.mdpi.com/2078-2489/15/12/794 |
| work_keys_str_mv | AT jonghoseol computationalanalysisofselfhealinginnanomaterialsusingneuralspikealgorithms AT jongyeopkim computationalanalysisofselfhealinginnanomaterialsusingneuralspikealgorithms AT abhilashkancharla computationalanalysisofselfhealinginnanomaterialsusingneuralspikealgorithms |