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<p>A chiral ruthenium probe (Ru-phe) for determination of amino acids was constructed by conjugating L-phenylalanine to a Ru-MOF precursor via EDC/NHS activation. The stereogenic center and aromatic side chain of L-Phe establish a chiral microenvironment that facilitates π–π stacking and hydrophobic interactions with tryptophan, thereby enabling efficient enantioselective recognition. The Ru-phe electrochemiluminescence (ECL) sensing platform achieved broad linear quantification ranges—0.1 nM – 1 mM for L-Trp and 10 nM – 1 mM for D-Trp. The detection limits were 0.06 nM for L-Trp and 13 nM for D-Trp. Moreover, the method showed strong reproducibility in human serum samples, with relative standard deviations (RSDs) of 0.9–1.8% for L-Trp. Machine-learning-assisted classification further improved enantiomer discrimination accuracy to 96%. Overall, this work introduces an efficient and highly sensitive ECL approach that enables practical enantiomeric determination of tryptophan, offering strong potential for clinical monitoring.</p> Graphical abstract <p></p>

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A newly developed chiral Ru-phe probe for highly sensitive enantioselective detection of tryptophan

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Yan Liu,
  • Zhaojiang Yin,
  • Xin Wang,
  • Binghui Li,
  • Quan Chen,
  • Yuqiang Chen,
  • Peng Tu,
  • Mei You,
  • Han Li,
  • Zubin Zhong,
  • Fusheng Liao,
  • Hao Fan,
  • Jing Zhang

摘要

A chiral ruthenium probe (Ru-phe) for determination of amino acids was constructed by conjugating L-phenylalanine to a Ru-MOF precursor via EDC/NHS activation. The stereogenic center and aromatic side chain of L-Phe establish a chiral microenvironment that facilitates π–π stacking and hydrophobic interactions with tryptophan, thereby enabling efficient enantioselective recognition. The Ru-phe electrochemiluminescence (ECL) sensing platform achieved broad linear quantification ranges—0.1 nM – 1 mM for L-Trp and 10 nM – 1 mM for D-Trp. The detection limits were 0.06 nM for L-Trp and 13 nM for D-Trp. Moreover, the method showed strong reproducibility in human serum samples, with relative standard deviations (RSDs) of 0.9–1.8% for L-Trp. Machine-learning-assisted classification further improved enantiomer discrimination accuracy to 96%. Overall, this work introduces an efficient and highly sensitive ECL approach that enables practical enantiomeric determination of tryptophan, offering strong potential for clinical monitoring.

Graphical abstract