Alzheimer’s disease (AD), Parkinson’s disease (PD) and glaucoma are all regarded as neurodegenerative diseases (neuro-DDs) because these diseases are highly related to the degeneration loss of functions and death of neurons with aging. The conventional diagnostic methods such as neuroimaging for these diseases are not only expensive but also time-consuming, resulting in significant financial burdens for patients and public health challenge for nations around the world. Hence early detection of neuro-DDs in a cost-effective and rapid manner is critically needed. For the past decades, some chip-based detection technologies have been developed to address this challenge, showing great potential in achieving point-of-care (POC) diagnostics of neuro-DDs. In this review, chip-based detection of neuro-DDs’ biomarkers enabled by different transducing mechanisms is evaluated.

We report a method to fabricate silicon micro–nanostructures of different shapes by tuning the number of layers and the sizes of self-assembled polystyrene beads, which serve as the mask, and by tuning the reactive ion etching (RIE) time. This process is simple, scalable, and inexpensive without using any sophisticated nanomanufacturing equipment. Specifically, in this work, we demonstrate the proposed process by fabricating silicon micro- or nanoflowers, micro- or nanobells, nanopyramids, and nanotriangles using a self-assembled monolayer or bilayer of polystyrene beads as the mask. We also fabricate flexible micro–nanostructures by using silicon molds with micro–nanostructures. Finally, we demonstrate the fabrication of bandage-type electrochemical sensors with micro–nanostructured working electrodes for detecting dopamine, a neurotransmitter related to stress and neurodegenerative diseases in artificial sweat. All these demonstrations indicate that the proposed process provides a low-cost, easy-to-use approach for fabricating silicon micro–nanostructures and flexible micro–nanostructures, thus paving a way for developing wearable micro–nanostructures enabled sensors for a variety of applications in an efficient manner.