Authors
Sergio de-la-Huerta-Sainz, Valentín Diez-Cabanes, Alberto Gutiérrez-Vega, Sara Santamaría, María A Escobedo, Pedro A Marcos, Alfredo Bol-Arreba, José L Trenzado, Mert Atilhan, Santiago Aparicio
Published in
ACS omega. Volume 11. Issue 26. Pages 38868-38891. Jul 07, 2026. Epub Jun 22, 2026.
Abstract
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 networka triangulation criterion requiring at least two independent method categories is essential. A quantitative structure-property framework is developed linking six hydrogen-bond descriptorsmotif population, persistence distribution, network connectivity, competitive hydration index, dynamic heterogeneity, and interfacial partitioningto 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 electrodeslargely neglected in prior studiesis critically examined. An integrated characterization workflow with standardized reporting criteria, validated force-field benchmarks, and data-driven descriptors for predictive screening is proposed.
PMID:
42428889
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.
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