Time Domain NMR in Polymer Science: From the Laboratory to the Industry

Quantification of microbial community assembly processes during degradation on diverse plastispheres based on physicochemical characters and phylogenetic bin-based null model analysis

To understand the differences in degradation processes depending on the chemical properties of polymers, it is necessary to both quantify the microbiome composition and evaluate the process of microbial turnover (i.e., community assembly processes) in a variety of polymer materials. In this study, using a phylogenetic bin-based null model analysis (i.e., iCAMP), we evaluated community assembly processes from original estuary water to 37 types of polymers, which provide overwhelmingly diverse niches for microbes, in 14-day incubation experiments. First, we evaluated the polymer properties related to degradation rates. Polymers with higher adipic acid (AdA) monomer exhibited higher motility, hydrophilicity, and degradation rates, whereas those with higher aromatic monomer exhibited the opposite trends. Second, microbiome composition analysis was performed, and the microbiomes were significantly changed by the AdA or aromatic content. This was consistent with the polymer properties, suggesting that polymer motility and hydrophilicity attributable to the first-order structure modify the accessibility of the enzyme to the reaction site and hence the degradation rate, resulting in differences in microbiome community composition. Finally, we determined community assembly processes from estuary water to plastics using a phylogenetic bin-based null model analysis. The importance of heterogeneous selection was higher in mobile, hydrophilic, and fast-degrading polymers, while that of homogeneous selection was lower. This suggests that the environmental difference between before and after incubation becomes significant under rapid degradation, which select microbes adapted to biofilm environments. In addition, the more stochastic turnover prevailed, the more variation in the communities (i.e., β-diversity) increased. This suggests that turnover processes not dictated by the environment lead to instability in community compositions.

Blood compatibility of poly(propylene glycol diester) and its water structure observed by differential scanning calorimetry and 2H-nuclear magnetic resonance spectroscopy

Recently, we applied solution 2H-nuclear magnetic resonance spectroscopy (2H NMR) to analyze the water (deuterium oxide, D2O) structure in several biopolymers at ambient temperature. We established that polymers with good blood compatibility (i.e. poly(2-methoxyethyl acrylate) (PMEA)) have water observed at high magnetic fields (upfield) compared with bulk water. Polymers containing poly(propylene glycol) (PPG) or poly(propylene oxide) (PPO) exhibit good compatibility; however, the reason for this remains unclear. In addition, reports on the blood compatibility of PPO/PPG are limited. Therefore, PPG diester (PPGest) was prepared as a model polymer, and its blood compatibility and water structure were investigated. PPGest exhibited excellent blood compatibility. The water in PPGest was observed upfield by 2H NMR, and it was defined as non-freezing water via differential scanning calorimetry. Based on these observations, the relationship between the blood compatibility and water structure of PPGest is discussed by comparing with those of PMEA, and the reason for the good performance of PPG/PPO-based polymers is discussed.

A fast and effective way to measure the inner pore size distributions of wetted cotton fibers and their pretreatment performance using time-domain nuclear magnetic resonance

This study reports the findings from using time-domain nuclear magnetic resonance (TD-NMR) to analyze the pore structures of cotton fibers. Cotton fibers, which swell and soften in water, present challenges for conventional pore measurement techniques. TD-NMR overcomes these by measuring the transverse relaxation time (T2) of water protons within the fibers, indicative of internal pore sizes. We established a T2-to-pore size conversion equation using mixed cellulose ester membranes. This enabled differentiation between strongly bound, loosely bound, and free water within the fibers, and detailed the water distribution. A method for measuring the pore size distribution of wet cotton fiber was developed using TD-NMR. We then examined how various pretreatments affect the fibers' internal pores by comparing their pore size distribution and porosity. Specifically, caustic mercerization primarily enlarges the porosity and size of larger pores, while liquid ammonia treatment increases porosity but reduces the size of smaller pores. This research confirms TD-NMR's utility in assessing cotton fabrics' wet processing performance.

Oleofoams: The impact of formulating air-in-oil systems from a lipid oxidation perspective

Air-in-oil foams, or oleofoams, have a great potential for food applications as they can at least partially replace animal or hydrogenated fats, without compromising on textural properties. Yet, there are some challenges to tackle before they can largely be implemented for real-life applications. One of those is the lack of data regarding their oxidative stability. This is an important point to consider, as although using oils rich in polyunsaturated fatty acids (PUFAs) is highly desirable from a nutritional perspective, these fatty acids are particularly prone to oxidation, which leads to major degradations of food quality. This work thus aimed to investigate the oxidative stability of oleofoams prepared with omega-3 PUFA-rich vegetable oils (rapeseed or flaxseed oil) and various types of high melting point lipid-based oleogelators (stearic acid, glyceryl monostearate and stearyl alcohol) when incubated at room temperature. The physical structure and stability of the oleofoams was monitored by various techniques (visual observations, microscopy, DSC, NMR, SAXS and WAXS). Lipid oxidation was assessed by combined measurements of primary (conjugated diene hydroperoxides) and secondary (thiobarbituric acid reactive substances - TBARS) products. We found that the oxidative stability of oleofoams was higher compared to that of the corresponding bulk oil. This protective effect was also found when the oil was simply mixed with the oleogelator without incorporation of air bubbles (i.e., forming an oleogel), and was somewhat modulated depending on the type of oleogelator. These results suggest that oleogelators and the structural changes that they induce limit the cascaded propagation of lipid oxidation in oil-continuous matrices, which is promising in the perspective of future applications.

Characterization of Controlled Release Starch-Nimodipine Implant for Antispasmodic and Neuroprotective Therapies in the Brain

Parenteral depot systems can provide a constant release of drugs over a few days to months. Most of the parenteral depot products on the market are based on poly(lactic acid) and poly(lactide-co-glycolide) (PLGA). Studies have shown that acidic monomers of these polymers can lead to nonlinear release profiles or even drug inactivation before release. Therefore, finding alternatives for these polymers is of great importance. Our previous study showed the potential of starch as a natural and biodegradable polymer to form a controlled release system. Subarachnoid hemorrhage (SAH) is a life-threatening type of stroke and a major cause of death and disability in patients. Nimotop® (nimodipine (NMD)) is an FDA-approved drug for treating SAH-induced vasospasms. In addition, NMD has, in contrast to other Ca antagonists, unique neuroprotective effects. The oral administration of NMD is linked to variable absorption and systemic side effects. Therefore, the development of a local parenteral depot formulation is desirable. To avoid the formation of an acidic microenvironment and autocatalytic polymer degradation, we avoided PLGA as a matrix and investigated starch as an alternative. Implants with drug loads of 20 and 40% NMD were prepared by hot melt extrusion (HME) and sterilized with an electron beam. The effects of HME and electron beam on NMD and starch were evaluated with NMR, IR, and Raman spectroscopy. The release profile of NMD from the systems was assessed by high-performance liquid chromatography. Different spectroscopy methods confirmed the stability of NMD during the sterilization process. The homogeneity of the produced system was proven by Raman spectroscopy and scanning electron microscopy images. In vitro release studies demonstrated the sustained release of NMD over more than 3 months from both NMD systems. In summary, homogeneous nimodipine-starch implants were produced and characterized, which can be used for therapeutic purposes in the brain.

Polymer composition optimization approach based on feature extraction of bound and free water using time-domain nuclear magnetic resonance

As global environmental sustainability becomes increasingly emphasized, the development of eco-friendly materials, including solutions to the issue of marine plastics, is thriving. However, the material parameter space is vast, making efficient search a challenge. Time-domain nuclear magnetic resonance offers material property information through the complex T₂ relaxation curves resulting from multiple mobilities. In this research, we used the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to evaluate the binding state of water (water affinity) in polymers synthesized with various monomer compositions, which were immersed in seawater. We also assessed the T₂ relaxation property of the polymers using the magic sandwich echo, double quantum filter, and magic-and-polarization echo filter techniques. We separated the T₂ relaxation curves of CPMG into free and bound water for polymers by employing semisupervized nonnegative matrix factorization. By employing the features of separated bound water and polymer properties, a polymer composition optimization method offered crucial factors to monomers through random forests, predicted the components of the polymer using generative topography mapping regression, and determined expected values using Bayesian optimization for polymer composition candidates with the desired high water affinity and high rigidity.

Usefulness of Applying Partial Least Squares Regression to T2 Relaxation Curves for Predicting the Solid form Content in Binary Physical Mixtures

This study applied partial least squares (PLS) regression to nuclear magnetic resonance (NMR) relaxation curves to quantify the free base of an active pharmaceutical ingredient powder. We measured the T₂ relaxation of intact and moisture-absorbed physical mixtures of tetracaine free base (TC) and its hydrochloride salt (TC·HCl). The obtained T₂ relaxation curves were analyzed by two methods, one using a previously reported T₂ relaxation time (T₂), and the other using PLS regression. The accuracy of estimating TC was inadequate when using previous T₂ values because the moisture-absorbed physical mixtures showed biphasic T₂ relaxation curves. By contrast, the entire measured whole of the T₂ relaxation curves was used as input variables and analyzed by PLS regression to quantify the content of TC in the moisture-absorbed TC/TC·HCl. Based on scatterplots of theoretical versus predicted TC, the obtained PLS model exhibited acceptable coefficients of determination and relatively low root mean squared error values for calibration and validation data. The statistical values confirmed that an accurate and reliable PLS model was created to quantify TC in even moisture-absorbed TC/TC·HCl. The bench-top low-field NMR instrument used to apply PLS regression to the T₂ relaxation curve may be a promising tool in process analytical technology.

The Potential of Myelin-Sensitive Imaging: Redefining Spatiotemporal Patterns of Myeloarchitecture

Recent advances in magnetic resonance imaging (MRI) have paved the way for approximation of myelin content in vivo. In this review, our main goal was to determine how to best capitalize on myelin-sensitive imaging. First, we briefly overview the theoretical and empirical basis for the myelin sensitivity of different MRI markers and, in doing so, highlight how multimodal imaging approaches are important for enhancing specificity to myelin. Then, we discuss recent studies that have probed the nonuniform distribution of myelin across cortical layers and along white matter tracts. These approaches, collectively known as myelin profiling, have provided detailed depictions of myeloarchitecture in both the postmortem and living human brain. Notably, MRI-based profiling studies have recently focused on investigating whether it can capture interindividual variability in myelin characteristics as well as trajectories across the lifespan. Finally, another line of recent evidence emphasizes the contribution of region-specific myelination to large-scale organization, demonstrating the impact of myelination on global brain networks. In conclusion, we suggest that combining well-validated MRI markers with profiling techniques holds strong potential to elucidate individual differences in myeloarchitecture, which has important implications for understanding brain function and disease.

Effect of Different Syrup Types on Turkish Delights (Lokum): A TD-NMR Relaxometry Study

Turkish delights were formulated by using sucrose (control) and different types of corn syrups (having varying glucose/fructose ratios) and allulose syrup. 30% allulose syrup and 30% sucrose-containing Turkish delights were found to exhibit an amorphous structure. Time-domain NMR relaxometry experiments were also conducted on delights by measuring T ₂ relaxation times, and two distinct proton populations were observed in all formulations. The use of different syrup types at different substitution levels led to significant changes in the relaxation times (T _2a and T _2b) of the samples, indicating that the relaxation spectrum might be used as a fingerprint for Turkish delights containing different types and amounts of syrup types. Second moment (M ₂) values which were measured from the signal acquired using a magic sandwich echo pulse sequence were also found to be an effective and promising indicator to detect the crystallinity of Turkish delights.

Porosity of Molecularly Imprinted Polymers Investigated by 129Xe NMR Spectroscopy

Molecularly imprinted polymers (MIPs) display intriguing recognition properties and can be used as sensor recognition elements or in separation. In this work, we investigated the formation of hierarchical porosity of compositionally varied MIPs using ¹²⁹Xe Nuclear Magnetic Resonance (NMR) and ¹H Time Domain Nuclear Magnetic Resonance (TD-NMR). Variable temperature ¹²⁹Xe NMR established the morphological variation with respect to the degree of cross-linking, supported by ¹H TD-NMR determination of polymer chain mobility. Together, the results indicate that a high degree of cross-linking stabilizes the porous structure: highly cross-linked samples display a significant amount of accessible mesopores that instead collapse in less structured polymers. No significant differences can be detected due to the presence of templated pores in molecularly imprinted polymers: in the dry state, these specific shapes are too small to accommodate xenon atoms, which, instead, probe higher levels in the porous structure, allowing their study in detail. Additional resonances at a high chemical shift are detected in the ¹²⁹Xe NMR spectra. Even though their chemical shifts are compatible with xenon dissolved in bulk polymers, variable temperature experiments rule out this possibility. The combination of ¹²⁹Xe and TD-NMR data allows attribution of these resonances to softer superficial regions probed by xenon in the NMR time scale. This can contribute to the understanding of the surface dynamics of polymers.

Unveiling the Role of PEO-Capped TiO2 Nanofiller in Stabilizing the Anode Interface in Lithium Metal Batteries

Lithium metal batteries (LMBs) will be a breakthrough in automotive applications, but they require the development of next-generation solid-state electrolytes (SSEs) to stabilize the anode interface. Polymer-in-ceramic PEO/TiO₂ nanocomposite SSEs show outstanding properties, allowing unprecedented LMBs durability and self-healing capabilities. However, the mechanism underlying the inhibition/delay of dendrite growth is not well understood. In fact, the inorganic phase could act as both a chemical and a mechanical barrier to dendrite propagation. Combining advanced in situ and ex situ experimental techniques, we demonstrate that oligo(ethylene oxide)-capped TiO₂, although chemically inert toward lithium metal, imparts SSE with mechanical and dynamical properties particularly favorable for application. The self-healing characteristics are due to the interplay between mechanical robustness and high local polymer mobility which promotes the disruption of the electric continuity of the lithium dendrites (razor effect).

The use of time domain 1 H NMR to study proton dynamics in starch-rich foods: A review

Starch is a major contributor to the carbohydrate portion of our diet. When it is present with water, it undergoes several transformations during heating and/or cooling making it an essential structure-forming component in starch-rich food systems (e.g., bread and cake). Time domain proton nuclear magnetic resonance (TD ¹ H NMR) is a useful technique to study starch-water interactions by evaluation of molecular mobility and water distribution. The data obtained correspond to changes in starch structure and the state of water during or resulting from processing. When this technique was first applied to starch(-rich) foods, significant challenges were encountered during data interpretation of complex food systems (e.g., cake or biscuit) due to the presence of multiple constituents (proteins, carbohydrates, lipids, etc.). This article discusses the principles of TD ¹ H NMR and the tools applied that improved characterization and interpretation of TD NMR data. More in particular, the major differences in proton distribution of various dough and cooked/baked food systems are examined. The application of variable-temperature TD ¹ H NMR is also discussed as it demonstrates exceptional ability to elucidate the molecular dynamics of starch transitions (e.g., gelatinization, gelation) in dough/batter systems during heating/cooling. In conclusion, TD NMR is considered a valuable tool to understand the behavior of starch and water that relate to the characteristics and/or quality of starchy food products. Such insights are crucial for food product optimization and development in response to the needs of the food industry.

Heparin-Superparamagnetic Iron Oxide Nanoparticles for Theranostic Applications

In this study, superparamagnetic iron oxide nanoparticles (SPIONs) were engineered with an organic coating composed of low molecular weight heparin (LMWH) and bovine serum albumin (BSA), providing heparin-based nanoparticle systems (LMWH@SPIONs). The purpose was to merge the properties of the heparin skeleton and an inorganic core to build up a targeted theranostic nanosystem, which was eventually enhanced by loading a chemotherapeutic agent. Iron oxide cores were prepared via the co-precipitation of iron salts in an alkaline environment and oleic acid (OA) capping. Dopamine (DA) was covalently linked to BSA and LMWH by amide linkages via carbodiimide coupling. The following ligand exchange reaction between the DA-BSA/DA-LMWH and OA was conducted in a biphasic system composed of water and hexane, affording LMWH@SPIONs stabilized in water by polystyrene sulfonate (PSS). Their size and morphology were investigated via dynamic light scattering (DLS) and transmission electron microscopy (TEM), respectively. The LMWH@SPIONs’ cytotoxicity was tested, showing marginal or no toxicity for samples prepared with PSS at concentrations of 50 µg/mL. Their inhibitory activity on the heparanase enzyme was measured, showing an effective inhibition at concentrations comparable to G4000 (N-desulfo-N-acetyl heparin, a non-anticoagulant and antiheparanase heparin derivative; Roneparstat). The LMWH@SPION encapsulation of paclitaxel (PTX) enhanced the antitumor effect of this chemotherapeutic on breast cancer cells, likely due to an improved internalization of the nanoformulated drug with respect to the free molecule. Lastly, time-domain NMR (TD-NMR) experiments were conducted on LMWH@SPIONs obtaining relaxivity values within the same order of magnitude as currently used commercial contrast agents.

Non-Conventional Time Domain (TD)-NMR Approaches for Food Quality: Case of Gelatin-Based Candies as a Model Food

The TD-NMR technique mostly involves the use of T1 (spin-lattice) and T2 (spin-spin) relaxation times to explain the changes occurring in food systems. However, these relaxation times are affected by many factors and might not always be the best indicators to work with in food-related TD-NMR studies. In this study, the non-conventional TD-NMR approaches of Solid Echo (SE)/Magic Sandwich Echo (MSE) and Spin Diffusion in food systems were used for the first time. Soft confectionary gelatin gels were formulated and conventional (T1) and non-conventional (SE, MSE and Spin Diffusion) TD-NMR experiments were performed. Corn syrups with different glucose/fructose compositions were used to prepare the soft candies. Hardness, °Brix (°Bx), and water activity (aw) measurements were also conducted complementary to NMR experiments. Relaxation times changed (p < 0.05) with respect to syrup type with no obvious trend. SE/MSE experiments were performed to calculate the crystallinity of the samples. Samples prepared with fructose had the lowest crystallinity values (p < 0.05). Spin Diffusion experiments were performed by using Goldman−Shen pulse sequence and the interface thickness (d) was calculated. Interface thickness values showed a wide range of variation (p < 0.05). Results showed that non-conventional NMR approaches had high potential to be utilized in food systems for quality control purposes.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring

Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra.

Materials informatics approach using domain modelling for exploring structure-property relationships of polymers

In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, ¹H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the ¹H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T₂ relaxation time for domains with different mobility. The average T₂ relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.

Additively manufactured, solid object structures for adjustable image contrast in Magnetic Resonance Imaging

The choice of materials challenges the development of Magnetic Resonance Imaging (MRI) phantoms and, to date, is mainly limited to water-filled compartments or gel-based components. Recently, solid materials have been introduced through additive manufacturing (AM) to mimic complex geometrical structures. Nonetheless, no such manufactured solid materials are available with controllable MRI contrast to mimic organ substructures or lesion heterogeneities. Here, we present a novel AM design that allows MRI contrast manipulation by varying the partial volume contribution to a ROI/voxel of MRI-visible material within an imaging object. Two sets of 11 cubes and three replicates of a spherical tumour model were designed and printed using AM. Most samples presented varying MRI-contrast in standard MRI sequences, based mainly on spin density and partial volume signal variation. A smooth and continuous MRI-contrast gradient could be generated in a single-compartment tumour model. This concept supports the development of more complex MRI phantoms that mimic the appearance of heterogeneous tumour tissues.

129Xe: A Wide-Ranging NMR Probe for Multiscale Structures

Porous materials are ubiquitous systems with a large variety of applications from catalysis to polymer science, from soil to life science, from separation to building materials. Many relevant systems of biological or synthetic origin exhibit a hierarchy, defined as spatial organization over several length scales. Their characterization is often elusive, since many techniques can only be employed to probe a single length scale, like the nanometric or the micrometric levels. Moreover, some multiscale systems lack tridimensional order, further reducing the possibilities of investigation. 129Xe nuclear magnetic resonance (NMR) provides a unique and comprehensive description of multiscale porous materials by exploiting the adsorption and diffusion of xenon atoms. NMR parameters like chemical shift, relaxation times, and diffusion coefficient allow the probing of structures from a few angstroms to microns at the same time. Xenon can evaluate the size and shape of a variety of accessible volumes such as pores, layers, and tunnels, and the chemical nature of their surface. The dynamic nature of the probe provides a simultaneous exploration of different scales, informing on complex features such as the relative accessibility of different populations of pores. In this review, the basic principles of this technique will be presented along with some selected applications, focusing on its ability to characterize multiscale materials.

NMR Profiling of Reaction and Transport in Thin Layers: A Review

Reaction and transport processes in thin layers of between 10 and 1000 µm are important factors in determining their performance, stability and degradation. In this review, we discuss the potential of high-gradient Nuclear Magnetic Resonance (NMR) as a tool to study both reactions and transport in these layers spatially and temporally resolved. As the NMR resolution depends on gradient strength, the high spatial resolution required in submillimeter layers can only be achieved with specially designed high-gradient setups. Three different high-gradient setups exist: STRAFI (STRay FIeld), GARField (Gradient-At-Right-angles-to-Field) and MOUSE (MObile Universal Surface Explorer). The aim of this review is to provide a detailed overview of the three techniques and their ability to visualize reactions and transport processes using physical observable properties such as hydrogen density, diffusion, T1- and T2-relaxation. Finally, different examples from literature will be presented to illustrate the wide variety of applications that can be studied and the corresponding value of the techniques.

In Situ RheoNMR Correlation of Polymer Segmental Mobility with Mechanical Properties during Hydrogel Synthesis

Understanding polymer gelation over multiple length-scales is crucial to develop advanced materials. An experimental setup is developed that combines rheological measurements with simultaneous time-domain ¹ H NMR relaxometry (TD-NMR) techniques, which are used to study molecular motion (<10 nm) in soft matter. This so-called low-field RheoNMR setup is used to study the impact of varying degrees of crosslinking (DC) on the gelation kinetics of acrylic acid (AAc) and N,N'-methylene bisacrylamide (MBA) free radical crosslinking copolymerization. A stretched exponential function describes the T₂ relaxation curves throughout the gelation process. The stretching exponent β decreases from 0.90 to 0.67 as a function of increasing DC, suggesting an increase in network heterogeneity with a broad T₂ distribution at higher DC. The inverse correlation of the elastic modulus G' with T₂ relaxation times reveals a pronounced molecular rigidity for higher DC at early gelation times, indicating the formation of inelastic, rigid domains such as crosslinking clusters. The authors further correlate G' with the polymer concentration during gelation using a T₁ filter for solvent suppression. A characteristic scaling exponent of 2.3 is found, which is in agreement with theoretical predictions of G' based on the confining tube model in semi-dilute entangled polymer solutions.

Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs

Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.

Monitoring the formation kinetics of a bicontinuous microemulsion

HYPOTHESES

The performance of bicontinuous microemulsions is usually assessed on the characteristics of the middle phase at equilibrium. However, applied to Enhanced Oil Recovery, such an evaluation would not be representative of the structure and composition of fluids in reservoir rocks. Studies on the properties of non-equilibrated microemulsions are still needed to better understand the formation of such complex systems, in particular to optimize input parameters of process simulation tools.

EXPERIMENTS

For this purpose, we monitored the formation of a microemulsion from contact with the oil to equilibrium when no mixing or convection is provided. Non-destructive methods such as Nuclear Magnetic Resonance, Micro-Computed Tomography, Dynamic Light Scattering and Small Angle X-ray scattering were used to extract the compositions, phase thicknesses, dynamics and structures of the system over time.

FINDING

We found that the system gets structured into several layers over time that include the transient presence of an oriented semi-crystalline phase. The growth of the bicontinuous middle phase results from a progressive reorganization of the liquid crystal. The compositional and structural gradients, observed along the sample height, are correlated and linked to the corresponding structures of the phase diagram of the quaternary system. Equilibrium is reached after the total transfer of the liquid crystal into the bicontinuous phase.

Dual stimuli-sensitive carrageenan-based formulation for additive manufacturing

The design and development of controlled release systems of molecules of interest (nutrients, flavors, and drugs) have attracted significant attention over several years. Herein, we report a formulation of dual temperature and electro responsive κ- and ι-carrageenan based hydrogel for efficient food material and drug delivery. The microstructure and the thermal behavior of the hydrogel were characterized. The in-vitro drug release from the hydrogel was also studied. Using this carrageenan-based formulation and folic acid as the drug model, a high drug loading, and a sustained release because of either electric field or temperature were observed. In principle, the proposed formulation does not rely on 3D printing to perform its function; however, it adds to the feedstocks for 3D printing in the food and pharmaceutical industries. For the future, this could allow potentially more complex smart structures to be created from this material, further tuning release behavior.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Nondestructive Investigation of the Agglomeration Process for Nanosuspensions via NMR Relaxation of Water Molecules

This study investigated an agglomeration of nanoparticles in a suspension using nuclear magnetic resonance (NMR) relaxation. The nanosuspension was prepared by wet bead milling using indomethacin and polyvinylpyrrolidone as an active pharmaceutical ingredient (API) and stabilizer, respectively. Transmission profiles using a dispersion analyzer based on multilight scattering technology confirmed that agglomeration occurred at 25 °C immediately after wet bead milling. In this study, we focused on the water molecules, not nanoparticles, and obtained the T₂ relaxation time (T₂) of the water molecules using the time-domain NMR (TD-NMR) technique. During the storage period, the T₂ value rapidly increased at the beginning of the storage. In a suspension system, because the T₂ value of water molecules is known to reflect the surface area of the particle, the observed rapid increase in T₂ value indicated an agglomeration of nanoparticles. Therefore, it was shown that the measurement of T₂ relaxation of a nanosuspension could evaluate the agglomeration process. This technique directly observes water molecules as opposed to nanoparticles. Thus, we believe that TD-NMR is a general-purpose technique that is independent of the type of API or polymer.

Proton Low Field NMR Relaxation Time Domain Sensor for Monitoring of Oxidation Stability of PUFA-Rich Oils and Emulsion Products

The nutritional characteristics of fatty acid (FA) containing foods are strongly dependent on the FA’s chemical/morphological arrangements. Paradoxically the nutritional, health enhancing FA polyunsaturated fatty acids (PUFAs) are highly susceptible to oxidation into harmful toxic side products during food preparation and storage. Current analytical technologies are not effective in the facile characterization of both the morphological and chemical structures of PUFA domains within materials for monitoring the parameters affecting their oxidation and antioxidant efficacy. The present paper is a review of our work on the development and application of a proton low field NMR relaxation sensor (¹H LF NMR) and signal to time domain (TD) spectra reconstruction for chemical and morphological characterization of PUFA-rich oils and their oil in water emulsions, for assessing their degree and susceptibility to oxidation and the efficacy of antioxidants. The NMR signals are energy relaxation signals generated by spin–lattice interactions (T₁) and spin–spin interactions (T₂). These signals are reconstructed into 1D (T₁ or T₂) and 2D graphics (T₁ vs. T₂) by an optimal primal-dual interior method using a convex objectives (PDCO) solver. This is a direct measurement on non-modified samples where the individual graph peaks correlate to structural domains within the bulk oil or its emulsions. The emulsions of this review include relatively complex PUFA-rich oleosome-oil bodies based on the aqueous extraction from linseed seeds with and without encapsulation of externally added oils such as fish oil. Potential applications are shown in identifying optimal health enhancing PUFA-rich food formulations with maximal stability against oxidation and the potential for on-line quality control during preparation and storage.

Time-domain NMR analysis for the determination of water content in pharmaceutical ingredients and wet granules

The application of time-domain NMR (TD-NMR) analysis to quantify water content in pharmaceutical ingredients is demonstrated. The initial phase of the study employed a range of disintegrants with defined amounts of added water (0-30% of the total weight) as samples; the disintegrants included croscarmellose sodium, corn starch, low-substituted hydroxypropyl cellulose, and crospovidone. After acquisition of the T₂ relaxation curves of the samples by TD-NMR measurements, these curves were analyzed by partial least squares (PLS) regression. According to the analysis, accurate and reliable PLS models were created that enabled accurate assessment of water content in the samples. A powder blend consisting of acetaminophen (paracetamol) and tablet excipients was also examined. Both a physical mixture of the powder blend and a wet granule prepared with a high-speed granulator were tested as samples in this study. Precise determination of water content in the powder blend was achieved by using the TD-NMR method. The accuracy of water content determination was equivalent to or better than that of the conventional loss on drying method. TD-NMR analysis samples were measured nondestructively and rapidly with low cost; thus, it could be a powerful quantitative method for determining water content in pharmaceuticals.

Synchronous Monitoring of Cement Hydration and Polymer Film Formation Using 1H-Time-Domain-NMR with T 2 Time-Weighted T 1 Time Evaluation: A Nondestructive Practicable Benchtop Method

The interactions between latex and cement are still not completely understood. In this work, we would like to address the temporal changes in cement hardening and latex film formation. For this reason, the hydration process and the film formation were simultaneously monitored. This scientific issue is even more challenging as a nondestructive quantitative analysis of the film formation process is not available yet. Here, we report on simultaneous monitoring of the latex film formation and the phase development in cementitious systems via ¹H-time-domain-NMR for the first time. The obtained results were validated using classical analytical methods, such as in situ X-ray diffraction, X-ray fluorescence (Rietveld analysis), and confocal laser scanning microscopy.

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, ¹³C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO₂. Using these methods, the ¹H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design.

Advanced spectroscopy, microscopy, diffraction and thermal analysis of polyamide adhesives and prediction of their functional properties with solid-state NMR spectroscopy

Advanced spectroscopy, microscopy, diffraction and thermal analysis reveal heterogeneity and dynamics in polyamide industrial adhesives; solid-state NMR spectroscopy enable the prediction of their functional properties.

Glycidyl azide polymer-based polyurethane vitrimers with disulfide chain extenders

Glycidyl azide polymer-based polyurethane vitrimers were synthesized. By optimizing the parameters, the vitrimers showed decent mechanical properties, healability and reprocessability. Fillers were loaded to synthesize healable composites.

Nano- to microscale three-dimensional morphology relevant to transport properties in reactive porous composite paint films

The quantitative evaluation of the three-dimensional (3D) morphology of porous composite materials is important for understanding mass transport phenomena, which further impact their functionalities and durability. Reactive porous paint materials are composites in nature and widely used in arts and technological applications. In artistic oil paintings, ambient moisture and water and organic solvents used in conservation treatments are known to trigger multiple physical and chemical degradation processes; however, there is no complete physical model that can quantitatively describe their transport in the paint films. In the present study, model oil paints with lead white (2PbCO₃·Pb(OH)₂) and zinc white (ZnO) pigments, which are frequently found in artistic oil paintings and are associated with the widespread heavy metal soap deterioration, were studied using synchrotron X-ray nano-tomography and unilateral nuclear magnetic resonance. This study aims to establish a relationship among the paints’ compositions, the 3D morphological properties and degradation. This connection is crucial for establishing reliable models that can predict transport properties of solvents used in conservation treatments and of species involved in deterioration reactions, such as soap formation.

Impact of the Solvent Quality on the Local Dynamics of Soft and Swollen Polymer Nanoparticles Functionalized with Polymer Chains

Grafting polymer chains on the surface of nanoparticles (NPs) is a strategy used to control the interaction between the NPs and their environment. The fate of the resulting particles in a given environment is strongly influenced by the solvent-polymer interaction. The solvent quality affects the behavior, conformation, and dynamics of the grafted polymer chains. However, when this polymer grafting strategy is used to functionalized polymer particles, the influence of solvent quality becomes even more complex; when the grafted polymer chains and the polymer nanoparticles are tethered together, the effect of the solvent quality on the behavior and dynamics of the system depends on the solvent interaction with both polymer components. To explore the relationship between the solvent quality and the dynamics of polymer-functionalized soft polymer NPs, we designed a system based on cross-linked polystyrene (PS) NPs grafted with a canopy of poly(methyl acrylate) (PMA). PS and PMA, two immiscible polymers, can be selectively solvated by using binary mixtures of solvents. NMR spectroscopy was used to address the effect of those selective solvents on the local mobility of the PS-PMA core-canopy NPs and revealed an interplay between the local mobility of the core and the local mobility of the canopy. A selective reduction of the solvent quality for the PMA canopy resulted in the expected reduction of the local mobility of the PMA chains, but also in the slower dynamics of the PS core. Similarly, a selective reduction of the solvent quality for the PS core resulted in a slower dynamics for both the PS core and the PMA canopy.

Short oligo(ethylene glycol) chain incorporated thermoresponsive microgels: from structural analysis to modulation of solution properties

Herein, we report synthesis of thermoresponsive poly(N-isopropylaccrylamide) (PNIPAM) microgels with short oligo(ethylene glycol) (OEG) chain comonomers (1 to 4/5 repeating unit) by surfactant-free precipitation copolymerization. The efficient incorporation of the comonomers was confirmed by a complete set of characterization methods viz., FTIR, 1H NMR, TEM, DLS, and viscometry. The structural heterogeneity and the distribution of the comonomers within the microgels were determined by means of 1H high-resolution transverse relaxation magnetization measurements. Interestingly, the incorporation of these short OEG chain comonomers led to the formation of a core-corona structure, in which the comonomers were mainly located in the core of the polymeric network with PNIPAM dangling chains at the microgel periphery. The experimental investigations of deswelling behaviours revealed that the OEG chains allowed precise control over the colloidal properties, including phase transition, particles size, swelling degree and polydispersity of the microgels. The tuneability of these properties that was interpreted in terms of polymeric hydrophobic/hydrophilic balance as well as structural diversity, could be achieved by changing the OEG chain length, comonomer feed and crosslinking density. Further, we found that the microgels with more hydrophilic OEG chains were able to show a higher relative swelling, and the same solid content thus led to a higher viscosity at all temperatures. The OEG chains remarkably improved the colloidal stability of the microgels in electrolyte solutions even at higher temperatures, thereby paving the way for the use of these microgels in a range of applications.

Some Applications of a Field Programmable Gate Array Based Time-Domain Spectrometer for NMR Relaxation and NMR Cryoporometry

NMR Relaxation (NMRR) is an extremely useful quantitative technique for material science, particularly for studying polymers and porous materials. NMR Cryoporometry (NMRC) is a powerful technique for the measurement of pore-size distributions and total porosities. This paper discusses the use, capabilities and application of a newly available compact NMR time-domain relaxation spectrometer, the Lab-Tools Mk3 NMR Relaxometer & Cryoporometer [Lab-Tools (nano-science), Ramsgate, Kent, UK (2019)]. Being Field Programmable Gate Array based means that it is unusually compact, which makes it particularly suitable for the lab bench-top, in the field and also mobile use. Its use with a variable-temperature NMR probe such as the Lab-Tools Peltier thermo-electrically cooled variable-temperature (V-T) probe is also discussed. This enables the NMRC measurement of pore-size distributions in porous materials, from sub-nano- to over 1 micron sized pores. These techniques are suitable for a wide range of porous materials and also polymers. This instrument comes with a Graphical User Interface (GUI) for control, which also enables both online and offline analysis of the measured data. This makes it is easy to use for material science studies both in the field and in university, research institute, company and even school laboratories. The Peltier cooling gives the precision temperature control and smoothness needed by NMR Cryoporometry, particularly near the probe liquid bulk melting point. Results from example NMR Relaxation and NMR Cryoporometric measurements are given.