Abstract
Polyolefins, which are vital materials in a wide range of industries, demand accurate and rapid microstructural analysis to enhance and optimize their performance characteristics. Triad sequence distributions are widely used to evaluate critical parameters, including comonomer content, monomer number-average sequence length, and the blockiness Koenig B value. While conventional algebraic methods for determining these values often lack accuracy, this study presents a more precise approach based on matrix operations. Traditional quantitative (13)C NMR has long served as the primary technique for analyzing polyolefin microstructures. However, its low sensitivity and lengthy acquisition time limit high-throughput analysis and hinder the practical determination of certain microstructural details. To overcome these limitations, we propose a synergistic approach that combines chromium-(III) acetylacetonate (Cr-(acac)(3)), a relaxation agent, with an artificial intelligence (AI)-optimized quantitative RINEPT (AIOQ-RINEPT) pulse sequence. Using a customized simulated annealing algorithm, a machine learning technique commonly used in AI model training, we optimized the variable delays τ(2) in the RINEPT sequence while keeping the delay τ(1) fixed. This optimization leads to uniform sensitivity enhancement across CH, CH(2), and CH(3) signals. The AIOQ-RINEPT technique, incorporating triply compensated 180° pulses (G5), ensures a broad excitation bandwidth. This method achieved a 7.5-fold increase in sensitivity, equivalent to a 56.3-fold reduction in acquisition time compared to conventional inverse-gated (13)C NMR. When combined with cryoprobe technology, a 41.3-fold improvement in sensitivity could be realized, resulting in a 1,706-fold decrease in acquisition time, making high-throughput analysis feasible. Experimental validation using a poly-(ethylene-co-1-butene) (EB) copolymer with a sufficiently high weight-average molecular weight (M (w) = 120,700 kg/mol) demonstrated accurate quantification of triad sequence distributions, comonomer content, and blockiness parameters. Two additional EB samples with lower weight-average molecular weights (M (w) = 86,000 and 58,000 kg/mol) were also employed to further validate the method. The method also effectively resolved signal overlap issues commonly encountered in samples with a high comonomer content. Moreover, the approach is broadly applicable to a wide range of polyolefins. This advancement enables rapid, automated (13)C NMR analysis of virgin and recycled polyolefins, allowing high-throughput characterization and sensitive detection of low-abundance features like long-chain branching (LCB). Additionally, the technique is suitable for analyzing low molecular weight saturated hydrocarbons, including Fischer-Tropsch products, such as waxes, lubricating oils, and jet fuel.