Competitive Hydrogen-Bond Partitioning in Deep Eutectic Solvents: From Cooperative Charge Spreading to Structure–Property Design Rules

High Resolution Image Download MS PowerPoint Slide Deep eutectic solvents (DESs) owe their remarkable melting-point depression, high viscosity, and tunable solvation to a hydrogen-bond network far richer than the binary donor–acceptor picture suggests. This study advances the framework of competitive hydrogen-bond partitioning: ionic Cl – ···H–X interactions, neutral donor–donor self-association, cation-mediated contacts, and water-competitive motifs coexist and continuously redistribute as a function of composition, temperature, and interfacial confinement. Evidence is synthesized from vibrational spectroscopy, multinuclear NMR, neutron and X-ray scattering, dielectric relaxation, classical and ab initio molecular dynamics, DFT cluster calculations, and machine-learning potentials, establishing that no single technique can fully characterize the network─a triangulation criterion requiring at least two independent method categories is essential. A quantitative structure–property framework is developed linking six hydrogen-bond descriptors─motif population, persistence distribution, network connectivity, competitive hydration index, dynamic heterogeneity, and interfacial partitioning─to viscosity, conductivity, diffusion, and glass transition across Type III, Type V, Natural DES (NADES), and hydrophobic DES. A central finding is the cooperativity–mobility tradeoff: cooperative charge spreading at Cl – simultaneously drives eutectic depression and network rigidity, defining a design axis along which DES can be rationally positioned. Water is analyzed as both competitive and cooperative partner across four hydration regimes, and interfacial hydrogen-bond reorganization at electrodes─largely neglected in prior studies─is critically examined. An integrated characterization workflow with standardized reporting criteria, validated force-field benchmarks, and data-driven descriptors for predictive screening is proposed.