인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Quantitative nuclear magnetic resonance (qNMR) spectroscopy could potentially be used for environmental microplastic analyses, provided the challenges posed by mixed polymer samples with varying concentrations and overlapping signals are understood. This study investigates the feasibility of qNMR as a reliable and cost-efficient method for quantifying synthetic polymers in mixtures of low and varying concentrations, addressing key challenges and limitations. Polymer mixtures were analysed using deuterated chloroform (CDCl<sub>3</sub>) and deuterated tetrahydrofuran (THF-d<sub>8</sub>) as solvents, with polystyrene (PS), polybutadiene-cis (PB), polyisoprene-cis (PI), polyvinyl chloride (PVC), polyurethane (PU), and polylactic acid (PLA) as selected polymers. Mixtures contained either low and high concentrations of each polymer or equal concentrations of all six polymers. Polymer concentrations were measured using the internal standard method. The method showed low relative errors for low concentrations of PS in CDCl<sub>3</sub> and PVC in THF-d<sub>8</sub>, with values of -5% and 0%, respectively, while PB and PI in CDCl<sub>3</sub> show relative errors of +5% and -3%, respectively. We observe significant linearity between nominal and measured concentrations with <i>R</i> <sup>2</sup> values ranging from 0.9655 to 0.9981, except for PU, which had high relative errors and poor linearity (<i>R</i> <sup>2</sup> = 0.9548). Moreover, simultaneous quantification of six polymers in THF-d<sub>8</sub> proves effective at intermediate concentrations. However, overlapping proton signals are observed, causing high-concentration polymers to mask low-concentration ones. While this study demonstrates low limit of quantification (LOQ) and advances in simultaneous polymer quantification, further research is needed to improve qNMR accuracy for mixed polymer samples and environmentally relevant concentrations.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.