Topological Materialization of Information via Spatiotemporal Resonance: Overcoming the Thermodynamic and Structural Limits of Computational Storage

As the global appetite for artificial intelligence infrastructure drives data ingestion toward the zettabyte scale, the fundamental limits of the von Neumann computing architecture form an insurmountable thermal and temporal bottleneck. Conventional "Storage-and-Forward" paradigms demand linear memory scaling (O(n)), precipitating critical resource depletion and excessive thermodynamic entropy (Landauer's limit) during petabyte-scale (PB) operations. In this paper, we present a radical convergence of mathematical topology, solid-state physics, and network communications: Spatiotemporal Coordinate Resonance (STCR) driven by the J.M. Resonance functon(R_JM). By structuring ingress data as mathematical topological coordinates mapped to a multi-dimensional Mersenne grid, our Hierarchical Spatiotemporal Key Generation (HSKG) algorithm facilitates a Stateless I/O Direct-Path, completely bypassing traditional operating system page-cache buffering. Empirical physical validation within a heterogeneous multi-OS node ecosystem (macOS/Windows) demonstrates that ingesting 1.0 PB of structured binary streams requires a static, non-fluctuating peak Random Access Memory (RAM) envelope of exactly 0.41 MB. This establishes the world's first proven constant space complexity (O(1)) materialization at the PB boundary. Furthermore, topological isomorphism ensures 100% deterministic data integrity post-materialization, authenticated by SHA-256 lattice verification. The thermodynamic and economic implications of decoupling storage capacity from transient memory are immense, offering an immediate trajectory to zero-RAM resilient frameworks for terrestrial generative AI supercomputers and antenna-less satellite infrastructure alike.