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ACS omega · 2026

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

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de-la-Huerta-Sainz Sergio, Diez-Cabanes Valentín, Gutiérrez-Vega Alberto, Santamaría Sara, Escobedo María A, Marcos Pedro A, Bol-Arreba Alfredo, Trenzado José L, Atilhan Mert, Aparicio Santiago

Аннотация

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.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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