Steric-Engineered Thermodynamic Gating: Metastable Assemblies for Chemical-Class Discrimination of Synthetic Cannabinoids.

Synthetic cannabinoids (SCs), a rapidly evolving class of new psychoactive substances (NPS), trigger severe neurotoxicity and fatalities while evading rapid, structure-selective on-site screening. To enable high-fidelity recognition of such chemically inert targets, we propose a generalizable "Steric-Engineered Thermodynamic Gating" strategy based on disaggregation-induced emission (DIE) mechanism that breaks the intrinsic "stability-sensitivity" deadlock in supramolecular sensing. Distinct from trial-and-error optimization, this approach rationally exploits steric bulk to induce active packing frustration, creating metastable aggregates designed to selectively detect SCs via synergistic non-covalent interactions (e.g., π-π stacking and hydrogen bonding). Functioning as a thermodynamic filter, this assembly remains inert against non-target interferents yet selectively undergoes cooperative disassembly upon binding with specific SCs via multivalent synergy, transforming a quenched "off" state into a robust blue-shifted "on" signal. Validating this strategy with EDMB-PINACA, the system exhibits ultrafast response (<1 s) and high sensitivity (LOD 4.7 µM); integrated into a 3D-printed portable chip, it enables reliable, false-positive-free screening in authentic samples (e.g., e-liquids, petals) with exceptional immunity to complex matrix interference. This work establishes a methodological blueprint for engineering aggregate metastability to recognize low-reactivity analytes, offering a theoretical foundation for designing intelligent field-deployable optics beyond the limitations of traditional molecular recognition.