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Frontiers in Analytical Science ·

Volume 6 - 2026 | https://doi.org/10.3389/frans.2026.1941270

Complementary techniques for investigating the molecular dynamics of uncrystallized water

  • Kitsakorn Locharoenrat1*corresponding author
  1. Department of Physics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
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Abstract

Uncrystallized water (UCW) refers to the fraction of water in hydrated materials that remains uncrystallized upon cooling. Rather than forming bulk ice, this water retains molecular mobility even below the nominal freezing point. In polymers and proteins, UCW includes strongly bound water associated with specific functional groups and more weakly confined water residing in nanoscale environments. As a result, it plays an important role in mechanical behavior, structural stability, and biological function. Because UCW is heterogeneous, no single technique can fully capture its properties, and multiple complementary methods are required. This review highlights how dielectric spectroscopy (DS), nuclear magnetic resonance (NMR), differential scanning calorimetry (DSC), neutron scattering, particularly quasielastic neutron scattering (QENS), and terahertz and infrared spectroscopies each probe different aspects of UCW across distinct time and different sample sizes. We outline what each technique measures, the models commonly used to interpret the data, and how combining these approaches helps distinguish between “free,” “bound,” and “non-freezable” water. Studies that apply multiple methods to the same samples, with consistent control of hydration and temperature, are essential for developing a coherent and quantitative picture of UCW. This integrated approach is also key to translating UCW behavior into practical design principles for cryopreservation, pharmaceutical freezing, and biomaterials. Importantly, complementarity arises from differences in timescale and the distinct correlation functions each method probes. Computational spectroscopy–ranging from classical molecular dynamics to emerging machine learning approaches–provides an atomistic link between dielectric, NMR, neutron, and vibrational measurements and shared descriptors of hydration dynamics.

Keywords

1 Introduction

Water makes up a large fraction of biological and polymer systems and is essential for maintaining biomolecular structure and function. Under confinement or at interfaces–such as in hydrated polymer networks, hydration shells around proteins, or nanometer-scale cavities–water can resist ice formation and remain amorphous well below its normal freezing point. In this state, it retains molecular mobility that is slower than bulk liquid water; however, markedly faster than that of a glassy solid (Soper, 2000; Chaplin, 2006; Ball, 2008).

Several terms in the literature are often used interchangeably, though they describe distinct concepts. “Hydration water” refers to water in the interfacial layer surrounding a solute or matrix. “Bound water” denotes water engaged in specific interactions, such as hydrogen bonding or ionic interactions, which restrict molecular mobility. “Non-freezable water” describes the fraction that remains amorphous upon cooling under a given experimental condition, such as calorimetry; in many systems, this is attributed to strongly interacting interfacial water. At certain biomaterial surfaces, water can form an intermediate hydration layer with mobility between that of bulk liquid water and tightly bound water. This layer is often associated with antifouling behavior, as it reduces protein adsorption (Rupley and Careri, 1991; Tanaka et al., 2013; Grosso et al., 2020; Akada et al., 2021; Nilsson, 2022; Saeker et al., 2023; Ma et al., 2022).

In this review, the classification of “freezable” and “non-freezable” water is inherently method-dependent. Faster cooling rates suppress nucleation and crystal growth, which can increase the apparent fraction of non-freezing water. In contrast, slower cooling or extended isothermal conditions allow more complete crystallization and shift these boundaries in the opposite direction. This distinction becomes important when comparing differential scanning calorimetry (DSC) measurements, which typically use controlled ramp rates, with dielectric spectroscopy (DS) or nuclear magnetic resonance (NMR) methods that may involve longer equilibration times at a fixed temperature (Ma et al., 2022; Liu et al., 2024). In addition to DS and NMR techniques discussed in this review, several other methods provide important insights into UCW. For instance, DSC quantifies the fraction of water that crystallizes, quasielastic neutron scattering (QENS) probes fast molecular motions at atomic length scales, and terahertz (THz) and infrared (IR) spectroscopies examine dynamics within the hydrogen-bond network. Molecular dynamics (MD) simulations complement these approaches by linking structure and motion at the atomic level. Together, these methods help translate the concept of “water fractions” into a more detailed molecular picture.

This review is important for the readers as it brings together the dispersed literature on uncrystallized water (UCW). Also, it presents how thermodynamics and dynamics are interpreted together, rather than separately. This review elucidates how confined water below Tf behaves across different systems that include the partially crystallized polymer/water mixtures and hydrated polymer or protein interfaces, where UCW remains disordered and mobile while ice forms nearby. By comparing methods such as DSC, DS, NMR, QENS, THz/IR, and MD, the review helps the readers see which techniques probe the time and length scales of UCW most effectively and where each method is complementary. Also, this review highlights the broader physical context by linking UCW to the anomalous behavior of supercooled water and the structural properties of water and ice that might give the readers a clearer mechanistic framework and identifying key gaps for future work.

This review reaches a new Frontier of the usual confinement- or surface-focused summaries and treats UCW below 0 °C as a coupled thermodynamic and dynamic phenomenon. It integrates the measurement methods (DSC, DS, NMR, QENS, THz/IR, MD). Also, it stresses the cross-validation across time and length scales rather than relying on a single technique. Unlike earlier reviews that mainly address confined water, hydrophobicity, or nanoconfinement in general, it brings together cryoscopic effects, pore geometry/topology, hydrophilic versus hydrophobic interfaces, and solute/co-solvent influences into one UCW-centered framework. It is also more specific in covering UCW in partially frozen soils, hydrated polymers, proteins, and interfacial water systems, which are often treated separately in the literature. In that sense, the novelty is not just the topic itself, but the unified perspective on how uncrystallized water persists, how it is measured, and how its behavior differs across materials and interfaces.

1.1 Colligative freezing-point depression versus uncrystallized water

In simple solutions, freezing-point depression is a classic colligative effect that reflects changes in the chemical potential of the solvent. In hydrated polymers and proteins, the situation is more complex. The key issue includes the equilibrium freezing temperature and what happens during cooling under non-equilibrium conditions. Ice may nucleate and grow in some regions, while water in others remains amorphous and dynamically active because it is confined and/or strongly coupled to the surrounding matrix. As a result, uncrystallized water (UCW) is governed by thermodynamics and by crystallization kinetics and nanoscale heterogeneity (Shinyashiki et al., 2007; Cerveny et al., 2010; Pastorczak et al., 2014; Sasaki et al., 2014; Sasaki et al., 2016; Shinyashiki et al., 2007; Fujii et al., 2020; Liu et al., 2024).

In this review, we use UCW to describe the fraction of water that remains disordered and dynamically active under conditions where bulk water would normally crystallize. This category includes tightly bound water at specific sites and more weakly confined water that stays amorphous due to nanoscale confinement. In practice, assessing UCW requires combining thermodynamic approaches, such as DSC-derived non-freezing water fractions, with dynamical methods, including DS, NMR, and QENS, that probe temperature- and hydration-dependent molecular mobility (Figure 1).

Schematic overview of UCW at temperatures below 0 °C. It shows the complementary time and length scales examined by DSC, DS, NMR, QENS, THz/IR, and MD. It highlights the contrast between disordered, mobile UCW at the protein/interface region and ice formation upon cooling in hydrated polymer/protein systems.
Figure 1. Schematic overview of UCW at temperatures below 0 °C. It shows the complementary time and length scales examined by DSC, DS, NMR, QENS, THz/IR, and MD. It highlights the contrast between disordered, mobile UCW at the protein/interface region and ice formation upon cooling in hydrated polymer/protein systems.

1.2 Biological significance of uncrystallized water

The relevance of UCW and related hydration states lies in the ability of interfacial water to control material properties that differ from those of bulk water. Hydration layers around proteins play key roles in stability, dynamic behavior, and molecular recognition at biointerfaces. In hydrated polymers and biomaterials, the amount and mobility of non-crystallizing water correlate with mechanical properties, transport behavior, and antifouling performance. A quantitative understanding of UCW distribution and its temperature-dependent dynamics is therefore important for cryoprotection, drug formulation, and biomaterial design (Ma et al., 2022; Liu et al., 2024; Sasaki et al., 2016; Panagopoulou et al., 2013; Du and Su, 2024; Fernández et al., 2024; Bagchi, 2005; Porion and Delville, 2020).

UCW is inherently heterogeneous, as water molecules experience environments ranging from strongly bound sites to mesoscopic confinement. The associated motions span a wide range of timescales, from hydrogen-bond fluctuations on the picosecond scale to glass-like structural relaxation in the order of seconds. As a result, characterizing UCW requires complementary methods that probe different types of molecular motion across these timescales. In this review, we first discuss DS (Section 2) and NMR (Section 3), followed by DSC, QENS, terahertz (THz) and infrared (IR) spectroscopy, and molecular dynamics (MD) approaches (Sections 4, 5, 6, and 7, respectively). We then consider how these methods complement one another, along with practical considerations and future directions (Section 8).

Figure 2 and Table 1 summarize the primary observables, accessible time and length scales, and key limitations of the methods discussed. The key point is not simply that some techniques operate on faster or slower timescales, but that each defines “water mobility” through a distinct correlation function or response. Figure 2 also illustrates how these methods span complementary measurement regimes, from ultrafast, localized motions (MD and computational spectroscopy, THz/IR, and QENS) to slower, more collective or bulk-sensitive responses (NMR, DS, and DSC).

Accessible time **(a)** and length scales **(b)** of complementary techniques used for UCW. MD, THz/IR, and QENS represent local/fast dynamics, while DS, NMR, and DSC represent bulk/slow relaxation.
Figure 2. Accessible time (a) and length scales (b) of complementary techniques used for UCW. MD, THz/IR, and QENS represent local/fast dynamics, while DS, NMR, and DSC represent bulk/slow relaxation.

TABLE 1. Physical observables, approximate time/length scales, representative UCW signals and drawbacks of DSC, dielectric spectroscopy, NMR, QENS, THz/IR spectroscopy, and MD/computational spectroscopy.

MethodPhysical observableApproximate windowUCW signalMain strengths and limitations
DSCHeat flow/enthalpy of freezing, melting, glass transitionThermal program sets the effective time window (seconds to hours); macroscopic sample averageReduced melting enthalpy, suppressed crystallization, residual amorphous T *g*Thermodynamic basis for water fractions; no molecular specificity and strongly protocol dependent
DSComplex permittivity ε*(ω) and dielectric loss ε″(ω)∼10–2 – 109 Hz (and higher with microwave extensions), corresponding to ∼100–10−11 s; bulk averagedBroad water-related relaxation persists below freezing and is shifted by one to several orders of magnitude toward slower timescales than bulk waterSensitive to collective dipolar dynamics; no intrinsic spatial resolution and assignments are model dependent
NMRRelaxation times, diffusion attenuation, chemical shifts/line shapesMicroseconds to seconds; angstrom-scale coordination to micrometer diffusion lengths depending on experimentShorter T 2, altered T 1 dispersion, reduced apparent diffusion, exchange-sensitive multi-pool behaviorChemical/isotopic specificity; exchange averaging and overlap with matrix resonances can complicate interpretation
QENSDynamic structure factor S (Q,ω) and elastic incoherent structure factor∼10–12 – 10–9 s and ∼10–11 – 10–8 mNarrower quasielastic linewidths, larger elastic fraction, reduced jump-diffusion/rotational rates relative to bulk waterDirect local probe of H-rich interfacial water; neutron access, isotope contrast, and model choice are major constraints
THz/IRLow-frequency collective modes and vibrational band shapes/shiftsSub-ps to few-ps dynamics; molecular to interfacial hydrogen-bond structureShifted/broadened THz absorption and OH-stretch redistribution relative to bulk water or iceStructural sensitivity to hydrogen-bond structures; strong water absorption, band overlap, and poor spatial resolution in linear measurements
MD/computational spectroscopyTrajectories, correlation functions, and forward-simulated spectraMethod dependent: Femtoseconds to microseconds; atomistic to tens-of-nm simulation cellsSpatially resolved mobility maps, residence times, hydrogen-bond lifetimes, and simulated DS/NMR/QENS/IR observablesMechanistic bridge across techniques; reliability depends on force fields, sampling, quantum effects, and validation against multiple experiments

2 Dielectric spectroscopy

DS measures the complex permittivity of a sample as a function of frequency and temperature. In hydrated materials, the dielectric response reflects dipole reorientation and other polarization processes arising from the matrix and the associated water. Multiple relaxation processes are typically observed. These can be linked to polymer chain dynamics, localized side-chain motions, and one or more populations of water molecules that remain mobile even below the normal freezing point. Because DS probes the bulk response of the sample, these assignments generally rely on systematic changes with hydration level, temperature, and composition, and are more robust when supported by complementary methods that provide information on water content or local structure (Shinyashiki et al., 2007; Cerveny et al., 2010; Pastorczak et al., 2014; Sasaki et al., 2014; Sasaki et al., 2016; Shinyashiki et al., 2007; Miyara et al., 2017; Fujii et al., 2020).

Specifically, DS measures the complex permittivity: ε * ω = ε ′ ω − ε ″ ω. This is defined as the frequency-domain response of the sample’s dipole moment to an oscillating electric field. In bulk water, the dielectric response is dominated by a relatively narrow, Debye-like relaxation in the microwave range. In contrast, UCW typically shows a broader, non-Debye loss peak at lower frequencies, reflecting slower and more heterogeneous molecular motion across interfacial environments. Crystalline ice, by comparison, largely suppresses the liquid-like dipolar response, leaving weaker contributions from defect motion and interfacial or Maxwell-Wagner polarization. In practice, a key indicator of mobile water in DS is the presence of a broad, water-related relaxation that persists below the freezing point and occurs at lower frequencies than that of bulk water at the same temperature (Shinyashiki et al., 2007; Cerveny et al., 2010; Pastorczak et al., 2014; Sasaki et al., 2014; Sasaki et al., 2016; Shinyashiki et al., 2007; Fujii et al., 2020; Latypova et al., 2020; Olsson et al., 2020; Hartl et al., 2022; Sugiyama et al., 2023).

DS is widely used because it can probe a broad range of dynamic processes. Broadband measurements typically span frequencies from about 10–2 – 109 Hz, while microwave techniques extend this range further, covering processes with characteristic times from roughly 100 s down to 10–11 s. This wide range comes with limited spatial resolution, as DS reports a bulk-averaged polarization response rather than local structure. Low-frequency data can be influenced by electrode polarization, and water-related signals may overlap with polymer α or β relaxations. In addition, a broad loss peak on its own cannot distinguish between a distribution of static environments and rapid exchange between them. As a result, DS is highly sensitive but also model-dependent, and its interpretation is more reliable when supported by complementary methods such as DSC, NMR, QENS, or simulation.

2.1 Polymer–water systems

Mixtures of water with hydrophilic polymers, such as PEG, PVP, and PVA, are commonly used as model systems in DS studies of UCW. Upon cooling, ice formation often removes much of the water-related dielectric signal, yet additional relaxation processes remain, indicating that a fraction of the water is still mobile. The relaxation times and activation behavior of these processes typically differ from those of bulk water, consistent with the effects of confinement and strong polymer–water interactions. Changes in polymer chemistry, crystallinity, and crosslinking can alter the fraction of “freezable” and “non-freezable” water and the overall dielectric response (Shinyashiki et al., 2007; Cerveny et al., 2010; Pastorczak et al., 2014; Sasaki et al., 2014; Sasaki et al., 2016; Shinyashiki et al., 2007; Fujii et al., 2020; Latypova et al., 2020; Olsson et al., 2020; Hartl et al., 2022; Sugiyama et al., 2023).

Partially crystallized PVP-water mixtures provide a clear example of how DS can distinguish between different types of motion. Broadband spectra are typically resolved into three main contributions: one associated with crystalline ice, one from UCW in the remaining amorphous regions, and a slower component arising from the polymer matrix. The UCW relaxation is generally slower than that of bulk liquid water but much faster than the polymer relaxation, indicating intermediate mobility that depends on water content and the sample’s thermal history (Cerveny et al., 2010; Fujii et al., 2020).

2.2 Biopolymer and protein systems

Similar dielectric behavior has been reported for biopolymers and proteins, including gelatin and GelMA hydrogels, hyaluronic acid matrices, and hydrated protein powders. In these systems, UCW often coexists with ice over a broad temperature range, and the remaining dielectric signal can be separated into contributions from constrained water and the biopolymer or protein matrix. The relative contributions depend on water content, network structure, and the extent of crystallization, so each material exhibits a distinct relaxation pattern (Shinyashiki et al., 2009; Ngai et al., 2013; Panagopoulou et al., 2013; Sasaki et al., 2014; Sasaki et al., 2016).

Recent broadband DS studies (2020–2023) have highlighted the heterogeneous nature of hydrated biomaterials and the need to relate dielectric relaxations to independently measured water fractions and local environments. Work combining DS with calorimetry, spectroscopy, and simulations has identified multiple bound and intermediate water populations. Some studies have also explored how collective hydration dynamics in protein-rich systems respond to applied electromagnetic fields (Latypova et al., 2020; Olsson et al., 2020; Hartl et al., 2022; Sugiyama et al., 2023).

For this reason, DS is best viewed as a highly sensitive probe of collective dipolar motion rather than a stand-alone tool for assigning specific water populations. It can clearly show that mobile water persists even after much of the sample has crystallized, but on its own it rarely identifies which molecules give rise to a particular spectral feature.

3 Nuclear magnetic resonance

NMR provides a versatile, molecule-specific set of tools for studying UCW, as it can probe local environments and rotational or translational mobility. Depending on the nucleus and pulse sequence used, NMR is sensitive to motions on timescales ranging from microseconds to seconds. It can also distinguish between different water populations based on differences in relaxation behavior, diffusion, and chemical shifts (Porion and Delville, 2020; Kress and Duer, 2025).

Most NMR measurements reflect filtered views of the spectral density, J(ω), associated with local magnetic interactions or molecular self-diffusion. In bulk liquid water, rapid motion averages out dipolar interactions, leading to long T 2 relaxation times, narrow signals, and high diffusion coefficients. In UCW, slower motion and exchange shortenT 2 values, can alterT 1 values, and reduce the apparent self-diffusion. In crystalline ice, signals are extremely broad with very shortT 2 values and are often not detected in standard liquid-state NMR experiments. NMR can probe scales from the atomic level to micrometer-scale diffusion and timescales from microseconds to seconds. However, interpretation can be challenging, as signals from different water populations may overlap, and contributions from the surrounding matrix can complicate the spectra (Mathur-De Vré, 1980; Stallmach and Galvosas, 2007; Korzhnev and Kay, 2008; Walderhaug et al., 2010; Porion and Delville, 2017; Porion and Delville, 2020; Schidlowski et al., 2020; Callaghan, 2022; Chaplin, 2022; Zhan et al., 2024; Kress and Duer, 2025; Barskiy et al., 2025).

For clarity, we group the NMR approaches most commonly used to study UCW into four categories: (1) relaxation measurements, including T 1, T 2, and related parameters; (2) self-diffusion coefficients obtained from pulsed field gradient methods; (3) chemical shifts and line shapes that reflect local hydrogen bonding and the surrounding atomic environment; and (4) emerging techniques such as zero-to ultralow-field (ZULF) NMR, which expand the range of water-containing soft materials that can be examined.

3.1 Relaxation times

Because spin-lattice (T 1) and spin-spin (T 2) relaxation times depend on molecular motion, they are useful for assessing water dynamics in hydrated systems. In polymers and proteins, relaxation often appears as multiple components, indicating the presence of several water populations with different mobilities, including tightly bound, intermediate, and bulk-like water. Approaches such as relaxation dispersion analysis, isotope substitution, and temperature-dependent measurements help refine these assignments and relate the observed timescales to underlying molecular processes (Korzhnev and Kay, 2008; Porion and Delville, 2017; Porion and Delville, 2020; Callaghan, 2022; Chaplin, 2022).

Beyond materials research, relaxation-based mapping is increasingly used in medicine and biology, where water mobility serves as an indicator of physiological state. For example, blood-water T 2 mapping has been explored as a noninvasive marker of metabolic status and subtle structural changes. These studies highlight the broader utility of relaxation measurements for probing water dynamics, even when they are not explicitly framed in terms of UCW (Schidlowski et al., 2020).

3.2 Diffusion coefficients

Pulsed field gradient (PFG) NMR, particularly pulsed-gradient spin-echo (PGSE) experiments, provide direct access to translational diffusion. In heterogeneous hydrated systems, deviations from single-exponential signal attenuation and the presence of restricted diffusion can reveal multiple water populations and characteristic confinement length scales. Diffusion measurements therefore complement relaxation data by helping to distinguish relatively mobile water from more slowly diffusing UCW confined within nanodomains or strongly interacting regions (Stallmach and Galvosas, 2007; Walderhaug et al., 2010).

3.3 Chemical shifts and local environments

Chemical shifts (1H and heteronuclear) and line shapes reflect local electronic environments and can provide indirect insights into hydrogen bonding, interactions with charged groups, and exchange between different environments. By tracking their temperature dependence and comparing to reference data, these spectral features reveal distinct hydration states and clarify how water interacts with solutes in complex systems (Mathur-De Vré, 1980; Du and Su, 2024; Zhan et al., 2024).

3.4 ZULF NMR and advanced relaxometry

ZULF NMR and advanced relaxometry methods reduce line broadening caused by susceptibility effects and enable measurements in regimes where slow spin dynamics and weak couplings become important. These approaches are well suited for probing slow processes and collective mobility in complex hydrated soft materials, and they complement conventional high-field NMR techniques (Barskiy et al., 2025).

3.5 Recent NMR applications to hydrated biomaterials

NMR studies have expanded UCW investigations to a wide range of biomaterials and biological systems, including collagen- and polymer-based scaffolds, membranes, and protein solutions. These studies combine relaxation, diffusion, and isotope-selective measurements, showing that water mobility is closely linked to mechanical properties, phase behavior, and stability in hydrated materials (Dubini et al., 2021; Roy et al., 2022; Fried et al., 2022; Fernández et al., 2024).

One example is the work by Chang et al., 2024 on gelatin methacrylate (GelMA) hydrogels, whereT 1 and T 2 relaxometry were used alongside DSC to probe bound and non-freezing water and relate these to microviscosity and diffusion. This combined NMR-DSC approach illustrates how integrating dynamic and thermodynamic measurements can improve the identification and interpretation of UCW in complex biomaterials (Chang et al., 2024).

4 Different scanning calorimetry

DSC does not measure molecular motion directly. Instead, it records differences in heat flow during controlled heating and cooling and uses these data to infer the state of water from associated enthalpy changes. In UCW studies, the melting enthalpy is often compared to that of bulk ice to estimate the fractions of freezable and non-freezable water. DSC can also detect glass transitions, cold crystallization, and depressed melting temperatures. These features help distinguish between water that remains non-crystalline and water that simply crystallizes outside the chosen temperature range (Panagopoulou et al., 2013; Ma et al., 2022; Liu et al., 2024; Chang et al., 2024).

Although DSC is valuable for tracking thermodynamic changes, it has several limitations. First, the measured fraction of “non-freezable water” is not fixed and can vary with factors such as scan rate, equilibration time, sample mass, pan sealing, and the reference enthalpy used for confined ice. Second, the cooling rate has a strong effect: rapid cooling can leave more water unfrozen, whereas slower cooling allows ice to nucleate at higher temperatures and reduces the remaining liquid fraction. For example, in sucrose-water systems, slower cooling shifts crystallization to higher temperatures and results in a lower apparent fraction of non-freezable water (Bogdanova et al., 2022). Similar sensitivity to experimental protocol should be expected when comparing DSC results with DS or NMR measurements that involve extended temperature equilibration.

DSC does not provide molecular or spatial specificity and offers limited insights into the kinetics of the remaining liquid once the thermal program is set. Even so, it remains essential, as any mechanistic interpretation of UCW from DS, NMR, QENS, or vibrational spectroscopy should be consistent with the fraction of water that DSC shows has remained uncrystallized under the same thermal history. In this sense, DSC defines the thermodynamic boundary conditions for the other techniques.

5 Quasielastic neutron scattering

QENS measures the dynamic structure factor, S (Q,ω), by analyzing the quasielastic broadening of scattered neutrons. Because incoherent scattering from hydrogen dominates, water dynamics are strongly emphasized, and selective deuteration can be used to suppress signals from either the solvent or the matrix. Depending on the instrument resolution, QENS typically probes timescales on the order of 10–12 to 10–9 s and length scales of 10–11 to 10–8 m, this makes it well suited for directly examining local translational jumps, rotational diffusion, residence times, and confinement at angstrom to nanometer scales (Sasaki et al., 2019; Yamada and Seto, 2020; Rahman et al., 2023; Kikuchi et al., 2024).

In bulk liquid water, the quasielastic signal is relatively broad because molecules undergo rapid translational and rotational motion. In ice, this broadening is greatly reduced and the signal becomes predominantly elastic, as translational diffusion no longer occurs on timescales within instrument measurement limitations. UCW and hydration water fall between these extremes: their spectra are narrower than those of bulk water, the elastic incoherent structure factor increases, and diffusion and rotational rates decrease. This reflects longer residence times near surfaces and more restricted rotational motion. In systems such as phospholipid membranes, QENS combined with selective deuteration has resolved distinct populations of hydration water, including free, loosely bound, and tightly bound fractions. These results show that the slow water component is not uniform but organized in a hierarchical manner (Yamada and Seto, 2020; Rahman et al., 2023).

The main limitation is that QENS is inherently local and model-dependent. Extracted diffusion coefficients and residence times depend on the chosen scattering model, the accessible Q range, and the instrument resolution, while motions much slower than the nanosecond timescale remains effectively invisible. Practical constraints, including limited access to neutron facilities, relatively large sample requirements, and the frequent need for isotope substitution, also restrict routine use. Even so, no other experimental method discussed here probes hydrogen-rich interfacial water as directly on angstrom and nanosecond scales.

6 Terahertz and infrared spectroscopies

THz and IR spectroscopies probe complementary aspects of hydrogen-bond network dynamics. THz time-domain spectroscopy (THz-TDS) and far-IR measurements are sensitive to low-frequency collective modes, including intermolecular stretching, hindered translations, vibrational motions, and collective rotational fluctuations. These modes typically fall in the ∼0.1–3 THz range for THz-TDS and extend to higher frequencies in the far-IR. These observables correspond to sub-picosecond to few-picosecond dynamics and are particularly sensitive to the strength of coupling between a hydration layer and its surrounding environment (Perakis et al., 2016; Hishida et al., 2023; Penkov, 2023).

Mid-IR spectroscopy provides a more local view of water structure. The O–H stretching band, typically in the 3000–3700 cm-1 shifts and redistributes as hydrogen bonds strengthen or weaken, while the bending mode near 1640 cm-1 offers additional insight into the local environment. As a result, IR spectroscopy characterizes water mainly in terms of hydrogen-bond geometries sampled by the oscillators, rather than by a single rotational correlation time, as in DS or NMR (Perakis et al., 2016).

Compared to bulk liquid water, UCW is often characterized by broadened and shifted low-frequency THz absorption, changes in the O-H stretching envelope, and the persistence of liquid-like spectral disorder below the nominal freezing point. In contrast to crystalline ice, these bands remain broader and less structured, reflecting a hydrogen-bond network that is still irregular and disordered. Hishida et al. (2023) showed that tightly bound and loosely bound hydration water can change in opposite ways during protein denaturation. This highlights that vibrational measures of “boundness” and calorimetric definitions of “non-freezable” water are related, but not equivalent.

These methods also have several limitations. First, water absorbs THz and IR radiation strongly, so measurements typically require very thin samples or careful background correction. Second, signals from water, ice, and the surrounding matrix can overlap, which complicates interpretation. Third, conventional linear techniques have limited spatial resolution, as they capture an average response over the illuminated region. As a result, THz and IR spectroscopies are most useful for probing hydrogen-bond structure and collective network dynamics rather than pinpointing the exact location of specific water populations.

7 Molecular dynamics simulations

MD simulations are valuable not only for tracking molecular trajectories but also for predicting experimentally observable signals. The autocorrelation of the total dipole moment determines the frequency-dependent dielectric response. Functions describing molecular orientation, exchange kinetics, and diffusion relate directly to NMR relaxation and PFG-NMR measurements. The intermediate scattering function, F (Q,t), and its Fourier transform connect to QENS, while dipole moment and polarizability correlation functions give rise to IR, THz, and surface-specific vibrational spectra. For UCW, this means that simulations can help to identify the origin of “slow water” signals; whether they arise from interfacial water, water confined within pores, water residing near charged groups for extended periods, or exchange between these environments (Bagchi, 2005; Johnson et al., 2009; Porion and Delville, 2020; Callaghan, 2022; Steinrücken et al., 2024; Westermayr and Marquetand, 2025; Wang and Cheng, 2025; Du et al., 2026).

This linking role is important because experimental measurements average over different spatial and temporal scales. For example, a broad loss peak in DS may reflect either a distribution of local environments or rapid exchange between a small number of distinct states. A short NMRT 2 can arise from truly immobilized water or from exchange broadening, while a slower QENS signal may result from shorter jump lengths, longer residence times, or a combination of both. MD simulations help disentangle these possibilities by providing access to parameters such as local residence times, hydrogen-bond lifetimes, rotational correlation functions, and spatially resolved diffusion within the same model system. Current best practice is to integrate simulations and experiments from the outset, testing simulations against multiple experimental observables rather than using them only for post hoc interpretation.

Considerable challenges remain. Common fixed-charge force fields can reproduce some bulk properties of water; however, they often fall short in describing dielectric relaxation, polarization effects, interfacial ordering, phase behavior, and hydrogen-bond dynamics. Most models also neglect nuclear quantum effects, proton transfer, and charge transfer. Ab initio MD offers higher accuracy but is limited to small systems and short timescales. Machine-learning potentials can extend simulations to larger systems; however, their reliability depends on the quality and diversity of the training data, including bulk, interfacial, supercooled, and partially crystallized states, as well as the proper treatment of long-range electrostatics and rare events (Westermayr and Marquetand, 2025; Wang and Cheng, 2025; Du et al., 2026).

For UCW, simulations should not be evaluated by a single diffusion coefficient. Instead, they should be assessed based on their ability to reproduce multiple experimental observables, including DSC-derived phase behavior, DS dielectric relaxation, NMR measures of rotational dynamics, as well as vibrational and QENS signatures. Only then can MD provide a meaningful mechanistic picture rather than serving as a visually appealing but limited molecular description.

8 Comparative analysis and future perspectives

The methods discussed above are complementary rather than redundant. Each probes a different physical property and therefore highlights different aspects of UCW. The main goal of combining these approaches is to link their outputs, for example, relating the non-freezable water fraction from DSC to relaxation features in DS or mobility populations identified using NMR, while maintaining consistent sample history and experimental conditions.

Figure 3 illustrates this concept in a simplified way. DSC, DS, NMR, and QENS are not simply alternative measures of the same “mobility.” Each technique reports on a distinct observable: DSC tracks enthalpy changes, DS reflects dipole correlations near q ≈ 0, NMR probes magnetic relaxation and diffusion, and QENS measures intermediate scattering at finite Q. Consistency across these methods is therefore not guaranteed and should be treated as a strong validation of any UCW assignment.

Qualitative representation of UCW methods expressed as different physical signals: **(a)** DSC, **(b)** DS, **(c)** NMR, and **(d)** QENS. Computational spectroscopy and MD use models to link these signals to shared molecular features. *Note:* All curves are schematic illustrations intended to convey general concepts and do not represent empirical data. All illustrations are original.
Figure 3. Qualitative representation of UCW methods expressed as different physical signals: (a) DSC, (b) DS, (c) NMR, and (d) QENS. Computational spectroscopy and MD use models to link these signals to shared molecular features. Note: All curves are schematic illustrations intended to convey general concepts and do not represent empirical data. All illustrations are original.

8.1 Complementary observables and timescales

The different methods discussed in this review can also be understood in terms of the underlying correlation functions they probe. DS is most sensitive to collective dipole reorientation, making it well suited for detecting a broad range of slow water relaxations, although it cannot unambiguously assign their chemical origin. NMR provides nucleus-specific information and reports the local rotational and translational motion, performing best when exchange between water populations occurs on intermediate timescales that remain experimentally distinguishable. DSC does not directly measure molecular motion; however, it helps to estimate how much water can realistically be assigned to UCW under a given thermal history. QENS probes very fast, localized motions of hydrogen-rich water, allowing one to assess whether the mobile fraction identified using DS and NMR remains dynamic at short times and length scales. THz and IR spectroscopies add a structural perspective by indicating whether this remaining water occupies a distinct hydrogen-bond network (Mathur-De Vré, 1980; Cerveny et al., 2010; Perakis et al., 2016; Sasaki et al., 2019; Porion and Delville, 2020; Yamada and Seto, 2020; Rahman et al., 2023; Hishida et al., 2023; Penkov, 2023; Kikuchi et al., 2024; Fernández et al., 2024).

MD and computational spectroscopy are distinct in that they can be directly connected to all of these experimental observables. For UCW to be accurately identified, it should ideally satisfy several conditions at the same time: the remaining liquid fraction should be thermodynamically consistent with DSC, its collective dipolar relaxation should agree with DS, its rotational and translational dynamics should align with NMR and/or QENS, and its hydrogen-bond structure should be consistent with vibrational spectra. Table 1 summarizes these roles, whereas Figure 3 highlights why no single curve or spectrum can fully capture the behavior of UCW.

8.2 Interpretation at ±1 °C

A temperature change from +1 °C to −1 °C should not be taken as evidence that water has entered a distinct liquid phase. In bulk water, a 2 °C shift near the freezing point produces only modest changes in viscosity and rotational dynamics, as long as the system remains liquid. In hydrated materials, the dominant factor in this range is usually the extent of ice nucleation and growth, which depends on impurities, confinement, thermal history, and the time available for crystallization. As a result, abrupt changes in DS, NMR, or calorimetric signals over this narrow temperature window, more often reflect redistribution between ice and the remaining interfacial water rather than the formation of a new UCW state.

This distinction is particularly important when comparing different experimental methods. Rapid cooling in DSC can preserve metastable liquid fractions that would not persist under the longer equilibration times typical of DS or NMR measurements near 0 °C. As crystallization proceeds in partially frozen systems, water may also redistribute from pore interiors to interfacial or nanoconfined regions. For meaningful comparisons, studies should report scan rates, hold times, how equilibration was verified, and the extent of crystallization when discussing behavior near 0 °C (Sasaki et al., 2016; Bogdanova et al., 2022; Liu et al., 2024; Chang et al., 2024).

8.3 Representative multi-technique case studies

The following case studies illustrate how combining multiple methods can move the field beyond descriptive classifications. The most informative examples are those in which different techniques do more than confirm the same conclusion, instead revealing complementary aspects of a shared physical picture (i.e., how much water remains unfrozen, where it is located, and which motions persist).

The first case study focuses on mesoporous silica. Brodrecht et al. (2018) combined solid-state NMR, broadband dielectric spectroscopy, and DSC to study lightly hydrated MCM-41, showing that the low-temperature liquid fraction cannot be understood without accounting for prior water redistribution during drying and rehydration. Time-dependent 1H NMR revealed initial bulk-like clusters that gradually shifted toward silanol-rich interfaces. DSC indicated that only part of the confined water crystallized, whereas broadband DS below 0 °C resolved two Arrhenius-type non-crystallizing processes rather than a single residual water population. Steinrücken et al. (2025) extended this work by combining broadband DS, 2H field-cycling NMR, and MD simulations for partially freezable confined water. Their results showed that crystallization preferentially removes more bulk-like water from pore centers, leaving behind a liquid fraction enriched in interface-associated molecules with a broader distribution of correlation times.55 In a related study of amino acid-functionalized silica pores, Steinrücken et al. (2025) used a similar combination of NMR and broadband DS to show that surface chemistry can slow water reorientation by up to two orders of magnitude compared to pristine silica. They also observed that activation energies increase from approximately 0.3 eV in the fully liquid regime to around 1.0 eV after partial crystallization, with a common low-temperature crossover near 185 K. Taken together, these findings indicate that the remaining liquid should not be viewed as a generic bound-water layer. Instead, the fraction that stays liquid after freezing is selectively stabilized by the surface, and its dynamics are governed by confinement, crystallization history, and surface chemistry.

The second case study focuses on phospholipid membranes and, in some respects, provides a more straightforward example. In this system, QENS, DSC, THz spectroscopy, and MD simulations have converged on a multiscale description of hydration water near lipid headgroups. Selective deuteration in QENS experiments have resolved distinct populations of free, loosely bound, and tightly bound water. The loosely bound fraction moves roughly in an order of magnitude more slowly than free interlamellar water, whereas the most strongly bound fraction exhibits dynamics on timescales comparable to those of the lipid headgroups themselves (Yamada and Seto, 2020; Rahman et al., 2023). MD studies of related membrane systems similarly show strongly reduced translational mobility near lipid headgroups. When considered alongside DSC and THz results, these findings support the presence of a distinct population of slowly moving hydration water in the interfacial region. The key point is that “intermediate” or “non-freezable” hydration water is not simply immobile. It can still undergo local rotational or jump-diffusive motion while remaining thermodynamically and structurally distinct from bulk liquid. Studies that truly integrate DS, NMR, DSC, QENS, vibrational spectroscopy, and simulation on the same specimen are still relatively rare, and this scarcity is telling. The most informative work is that which combines thermodynamic quantities, dynamic timescales, and hydrogen-bond structure into a single coherent framework, rather than redefining “free,” “bound,” and “non-freezable” water separately for each method.

8.4 Outlook

Progress in UCW research is likely to come less from collecting more data with a single technique and more from coordinated studies that combine multiple methods. At a minimum, such work should report water content, sample preparation, cooling and heating protocols, hold times, temperature calibration, and whether the same sample was examined before and after partial crystallization. Without this level of detail, it remains difficult to compare results from DSC, DS, NMR, neutron scattering, and vibrational spectroscopy, even when nominal compositions are identical. Furthermore, UCW is best understood as a coupled confinement-interface-solute phenomenon rather than a single process (Figure 4). Recent studies show that nanoscale confinement could modify freezing and melting behaviors, promote layered or quasi-liquid interfacial water, and create phase behavior strongly dependent on pore size, pore geometry, and wall hydrophilicity (Jia et al., 2025; Ran et al., 2026a; Ran et al., 2026b). Surface chemistry and topology are able to control UCW by modifying the interfacial water structures, hydrogen-bond networks, wettability, and ice nucleation pathways (Lenz et al., 2025a; Lenz et al., 2025b; Bushuev et al., 2026). In parallel, salts and mixed-solvent systems are able to shift the freezing equilibria by depressing freezing points, modifying hydration and partitioning, and enhancing unfrozen-water-film thickness under confinement (Turov et al., 2025; Mohammed et al., 2025; Ran et al., 2026c). A recent study also indicates that dynamic characterization remains important for understanding UCW at T < Tf, with NMR and MD used to quantify unfrozen water and its low-temperature behavior (Ren et al., 2025). Together, these findings support the view that UCW arises from interfacial thermodynamics in confined spaces. At the same time, additional dynamic probes, such as thermally stimulated depolarization current, could further improve understanding of water dynamics below freezing.

Schematic workflow that shows how UCW is probed in confined systems. NMR is used to quantify unfrozen water content and pore-water distributions. MD is used to determine the molecular mechanisms underlying interfacial layering, hydrogen bonding, and mobility that control UCW behavior.
Figure 4. Schematic workflow that shows how UCW is probed in confined systems. NMR is used to quantify unfrozen water content and pore-water distributions. MD is used to determine the molecular mechanisms underlying interfacial layering, hydrogen bonding, and mobility that control UCW behavior.

Another important direction is the closer integration of computational spectroscopy with experimental data. Rather than using simulations only to visualize interfacial structure, it is now possible to compute dielectric spectra, NMR-related correlation functions, neutron scattering observables, and vibrational responses from the same trajectories. Advances in machine learning–based spectroscopy and molecular dynamics make it feasible to access longer timescales and larger interfacial systems while retaining sufficient accuracy for direct comparison with experiment (Westermayr and Marquetand, 2025; Wang and Cheng, 2025; Du et al., 2026). This is particularly valuable for UCW, where relevant water populations are heterogeneous, metastable, and often governed by rare exchange events that are difficult to capture experimentally or in short Ab initio simulations.

At the same time, data-driven approaches should complement, not replace, physical interpretation. The most useful machine-learning models will be trained on well-defined benchmark datasets that combine raw spectra, relaxation data, calorimetric measurements, and validated simulation trajectories collected under controlled thermal conditions. When paired with clear molecular descriptors, such as residence times, hydrogen-bond statistics, orientational order, local diffusion, and exchange rates, these approaches can move beyond simple classification and support predictive design in areas such as cryoprotection, antifouling materials, pharmaceutical freezing, and biomimetic systems.

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Footnotes

Edited by: Kun Zhang, School of Medicine, Shanghai Jiao Tong University, China

Reviewed by: Hongding Zhang, Xinyang Normal University, China

Volodymyr Gun'Ko, Chuiko Institute of Surface Chemistry (NAN Ukraine), Ukraine

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Author contributions

KL: Writing – review and editing, Writing – original draft.

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Locharoenrat, K. (2026). Complementary techniques for investigating the molecular dynamics of uncrystallized water. Frontiers in Analytical Science, Volume 6. https://doi.org/
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*Correspondence: Kitsakorn Locharoenrat

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