Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At $$45\,^{\circ }\hbox {C}$$ , logP predominantly influenced retention, akin to reversed-phase columns, while at $$5^{\circ }\hbox {C}$$ , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.

中文翻译：

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通过机器学习算法对 HPLC 温度响应聚合物柱的保留机制进行物理化学建模


温度响应液相色谱 (TRLC) 利用纯水作为流动相，无需使用有害有机溶剂，为环境友好型分析技术提供了反相液相色谱 (RPLC) 的一种有前景的替代方案。 TRLC 色谱柱填充有与二氧化硅颗粒偶联的温度响应型聚合物，表现出受温度诱导的聚合物水合影响的独特保留机制。研究驱动高温和低温分离的物理化学参数对于更好的色谱柱制造和选择性控制至关重要。使用在不同温度下分析的 139 个分子的数据集进行可预测性评估，阐明了与保留机制相关的分子描述符 (MD)。对线性回归、支持向量回归 (SVR) 和基于树的集成模型进行了评估，但没有表现突出的。通过 r 和平均绝对误差 (MAE) 等指标以及统计分析来验证模型的精度、准确度和稳健性。在 $$45\,^{\circ }\hbox {C}$$ 处，logP 主要影响保留，类似于反相柱，而在 $$5^{\circ }\hbox {C}$$ 处，与亲脂性和阴性 MD 以及特定的官能团决定了保留。这些发现为 TRLC 机制提供了更深入的见解，促进方法开发并最大限度地发挥色谱柱潜力。