A novel capacitive sensor based on molecularly imprinted nanoparticles as recognition elements
A novel capacitive sensor based on molecularly imprinted nanoparticles as recognition elements
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DOI：
10.1016/j.bios.2018.07.070
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发表时间：
2018-11-30
影响因子：
12.6
通讯作者：
Mattiasson, Bo
中科院分区：
文献类型：
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作者：
Canfarotta, Francesco;Czulak, Joanna;Mattiasson, Bo
分子印迹聚合物（MIP）是能够选择性结合其靶（模板）分子的合成受体，并且因此用作测定和传感器中的识别元件，作为相对不稳定的酶和抗体的替代物。在此，我们描述了一种用于将MIP纳米颗粒（nanoMIPs）与（无标记）电容传感器集成的制造友好的方案。通过固相合成具有不同尺寸和性质的两种模板，包括小分子四氢大麻酚（THC）和蛋白质（胰蛋白酶），产生纳米MIPs。将纳米MIP沉积在传感器的表面上，并测量结合靶时的电容变化（Δ C）。选择性和检测限的显著改善（与先前使用的MIP微粒相比一个数量级）可以归因于它们增加的表面积与体积比和通过固相方法产生的纳米MIP的更高特异性。所描述的方法也与常见的传感器制造方法兼容，与涉及原位MIP聚合的方法相反。所提出的传感器显示出高选择性，快速的传感器响应（45分钟，包括注射，再生和再平衡与运行缓冲液），和简单的数据分析，这使得它可行的无标记监测在real-dine。本手稿中评估的目标集显示了生物传感器平台的普遍适用性。
Molecularly Imprinted Polymers (MIPs) are synthetic receptors capable of selective binding to their target (template) molecules and, hence, are used as recognition elements in assays and sensors as a replacement for relatively unstable enzymes and antibodies. Herein, we describe a manufacturing-friendly protocol for integration of MIP nanoparticles (nanoMlPs) with a (label-free) capacitive sensor. The nanoMlPs were produced by solid-phase synthesis for two templates with different sizes and properties, including a small molecule tetrahydrocannabinol (THC) and a protein (trypsin). NanoMIPs were deposited on the surface of the sensor and the change in capacitance (Delta C) upon binding of the target was measured. The significant improvement in the selectivity and limit of detection (one order of magnitude compared to previously used MIP microparticles) can be attributed to their increased surface-to-volume ratio and higher specificity of the nanoMlPs produced by the solid-phase method. The methodology described is also compatible with common sensor fabrication approaches, as opposed to methods involving in situ MIP polymerisation. The proposed sensor shows high selectivity, fast sensor response (45 min including injection, regeneration and re-equilibration with running buffer), and straightforward data analysis, which makes it viable for label-free monitoring in real-dine. The set of targets assessed in this manuscript shows the general applicability of the biosensor platform.