When the MOUSE leaves the house

Magnetic resonance insights into the heterogeneous, fractal-like kinetics of chemically recyclable polymers

Moving toward a circular plastics economy is a vital aspect of global resource management. Chemical recycling of plastics ensures that high-value monomers can be recovered from depolymerized plastic waste, thus enabling circular manufacturing. However, to increase chemical recycling throughput in materials recovery facilities, the present understanding of polymer transport, diffusion, swelling, and heterogeneous deconstruction kinetics must be systematized to allow industrial-scale process design, spanning molecular to macroscopic regimes. To develop a framework for designing depolymerization processes, we examined acidolysis of circular polydiketoenamine elastomers. We used magnetic resonance to monitor spatially resolved observables in situ and then evaluated these data with a fractal method that treats nonlinear depolymerization kinetics. This approach delineated the roles played by network architecture and reaction medium on depolymerization outcomes, yielding parameters that facilitate comparisons between bulk processes. These streamlined methods to investigate polymer hydrolysis kinetics portend a general strategy for implementing chemical recycling on an industrial scale.

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well-established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal-organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high-throughput, non-destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

A Portable Chip-Based NMR Relaxometry System With Arbitrary Phase Control for Point-of-Care Blood Analysis

In this article, we present a portable NMR relaxometry system optimized for the point-of-care analysis of body liquids such as blood. The presented system is centered on an NMR-on-a-chip transceiver ASIC, a reference frequency generator with arbitrary phase control, and a custom-designed miniaturized NMR magnet with a field strength of 0.29 T and a total weight of 330 g. The NMR-ASIC co-integrates a low-IF receiver, a power amplifier, and a PLL-based frequency synthesizer on a total chip area of 1100 × 900 μm 2. The arbitrary reference frequency generator enables the use of conventional CPMG and inversion sequences, as well as modified water-suppression sequences. Moreover, it is used to implement an automatic frequency lock to correct temperature-induced magnetic field drifts. Proof-of-concept measurements on NMR phantoms and human blood samples show an excellent concentration sensitivity of v[Formula: see text] = 2.2 mM/[Formula: see text]. This very good performance renders the presented system an ideal candidate for the future NMR-based point-of-care detection of biomarkers such as the blood glucose concentration.

Automated optimization of spatial resolution for single-sided NMR

Single-sided NMR instruments utilize inhomogeneous magnetic fields with strong gradients to nondestructively probe physical properties of materials. The sensitive region of this type of magnet is often a thin slice above the magnet's surface; measuring planar samples with high spatial resolution requires coplanarity between the sensitive region of the magnet and the sample region of interest. We developed an algorithmic approach to position flat samples coplanar with the magnet's sensitive region. The efficient and objective positioning process utilizes an adjustable stage that offers control over three degrees of freedom, and the optimal position for each sample is found with a quadtree algorithm. We show this algorithm is effective for positioning samples with various relaxation behaviors. We report resolution values that describe position optimization, acquisition constraints, and final spatial resolution for each sample. Measurements after optimized positioning had appropriate spatial resolution to distinguish physical regions of layered samples with different physical properties, namely, relaxation behavior. Our algorithmic positioning process can be implemented for planar samples in research and industrial settings to enhance spatial resolution of single-sided NMR measurements.