Graphene Spin (CP364): Detection of Local Temporal Instability Across Heterogeneous Systems

This dossier presents Graphene Spin (CP364), a methodological framework for the analysis of local temporal instability in complex systems through structured time-series analysis. The work proposes a reproducible computational approach designed to identify dynamic regime transitions, local instability patterns, and progressive loss of temporal regularity preceding observable macroscopic changes. The methodology is based on the hypothesis that local temporal instability represents an informational dimension distinct from traditional global metrics such as degradation, efficiency loss, or performance decay. The framework focuses on the evolution of the signal over time rather than on the instantaneous state of the system. The study analyzes heterogeneous domains, including: electrochemical battery datasets (NASA PCoE), electrical system temporal signals, multi-domain dynamic systems. The computational pipeline is structured around three principal modules: GSD13 — Temporal Pattern Analysis GSD14 — Degradation Validation GSD15 — Instability Validation The framework uses sliding-window analysis, local derivative metrics, regime classification, and instability aggregation to transform continuous signals into sequences of dynamic states. The principal regimes identified are: STATIC BALANCED TRANSITION DOMINANT One of the main results of the study is the emergence of recurring temporal structures across domains characterized by very different dynamics. In both electrochemical and electrical systems, the analysis reveals coherent sequences of the form: BALANCED → TRANSITION → DOMINANT suggesting that instability evolves progressively rather than appearing instantaneously. The work further shows that local instability may increase even when traditional global indicators remain apparently stable, indicating that temporal dynamics and global system state represent distinct but complementary informational layers. The release includes: full dossier documentation, computational scripts, structured outputs, CSV/JSON summaries, ranking tables, MEMTRIN tracking structures, reproducibility notes, SHA256 integrity verification files. The methodology is intentionally domain-independent and does not require: explicit physical models, supervised labeling, domain-specific assumptions. Graphene Spin (CP364) is presented as a computational and methodological framework for exploratory analysis of temporal dynamics in complex systems. It is not proposed as a validated physical theory nor as an experimental graphene technology platform. The results suggest that local temporal coherence and its progressive degradation may represent a general structural property of heterogeneous systems approaching critical transitions.