Biomolecule-gated mesoporous materials with electrochemically triggered optical response for the sensitive, simple and rapid determination of trace contaminants in the turbid and complex matrix milk

目的是制备一种新型杂交生物分子门控材料，用于快速、灵敏地识别牛奶中的关键真菌毒素和抗生素。这些材料的智能设计依赖于大量的电化学发光（ECL）指示分子被限制在高度多孔的纳米颗粒中，这些纳米颗粒被附着在外表面的几个分析物响应帽所封闭。然后，分析物的出现打开了孔隙的盖子，从而释放了存储的指标和有效的信号放大。这些材料将被整合到横向流动格式中，使用预打印的微电极进行快速和敏感的蘸棒分析，允许使用智能手机等便携式设备进行电化学激发和光学询问。这样的ECL检测将保证在不需要样品预处理的混浊和（生物）化学复杂的基质牛奶中的超痕量分析灵敏度。除了蘸料棒，微流控平台也将被开发出来，用于非透明液体中目标化合物的潜在在线监测。不同的看门人化学（抗体，适体）将进行比较，ECL信号将在选择性，灵敏度，动态范围和响应时间方面与传统荧光检测进行基准测试。直接在具有挑战性的基质奶中同时定量真菌毒素和抗生素的宏伟目标将成为这一有前途的新检测范式的关键试验台。

The aim is to prepare new hybrid biomolecule-gated materials for the rapid and sensitive recognition of key mycotoxins and antibiotics in milk. The smart design of these materials relies on a larger number of electrochemiluminescent (ECL) indicator molecules being confined in highly porous nanoparticles, which are closed by few analyte-responsive caps attached to the outer surface. Advent of the analyte then uncaps the pores, leading to release of the stored indicators and efficient signal amplification. The materials will be incorporated into lateral flow formats for rapid and sensitive dip-stick assays with pre-printed microelectrodes, allowing for both, electrochemical excitation and optical interrogation with portable devices such as smartphones. Such ECL detection will guarantee ultratrace analytical sensitivity in the turbid and (bio-)chemically complex matrix milk without sample pretreatment. Besides dip-sticks, microfluidic platforms for the potential online monitoring of target compounds in nontransparent liquids will be developed. Different gatekeeper chemistries (antibodies, aptamers) will be compared, and ECL signaling will be benchmarked against conventional fluorescence detection in terms of selectivity, sensitivity, dynamic range and response time. The ambitious goal of simultaneously quantifying mycotoxins and antibiotics directly in the challenging matrix milk will serve as a critical test-bed for this promising new detection paradigm.