Scholarlink

<p>As electromagnetic pulse (EMP) threats, from nuclear bursts to solar flares, intensify, silicon-based data storage risks catastrophic failure, with models forecasting 90% loss in unprotected systems. We propose a hybrid data storage framework that combines conventional silicon systems with biomolecular and neuromorphic technologies, aiming to substantially improve resilience against electromagnetic pulse disruptions. Deoxyribonucleic Acid (DNA) archives, when properly encapsulated and physically isolated from conductive pathways, exhibit negligible susceptibility to EMP electric fields up to tens of kV/m; however, their long-term integrity remains contingent on protection against ionizing radiation and adverse environmental conditions. Through AI-driven CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) encoding and optogenetic bridges, this approach enables data to persist, adapt, and be recovered even when conventional electronic infrastructures fail, vital for humanity’s sustainable future amid rising uncertainties.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Emp-secure data storage through biohybrid and neuromorphic paradigms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

查看全文
  • Saeed Banaeian Far,
  • Mohammad Reza Chalak Qazani,
  • Azadeh Imani Rad

摘要

As electromagnetic pulse (EMP) threats, from nuclear bursts to solar flares, intensify, silicon-based data storage risks catastrophic failure, with models forecasting 90% loss in unprotected systems. We propose a hybrid data storage framework that combines conventional silicon systems with biomolecular and neuromorphic technologies, aiming to substantially improve resilience against electromagnetic pulse disruptions. Deoxyribonucleic Acid (DNA) archives, when properly encapsulated and physically isolated from conductive pathways, exhibit negligible susceptibility to EMP electric fields up to tens of kV/m; however, their long-term integrity remains contingent on protection against ionizing radiation and adverse environmental conditions. Through AI-driven CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) encoding and optogenetic bridges, this approach enables data to persist, adapt, and be recovered even when conventional electronic infrastructures fail, vital for humanity’s sustainable future amid rising uncertainties.