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This work describes how selective patterning of hydrophobic and hydrophilic areas inside microchannels of microfluidic devices can be achieved by combining well-known chemical protocols and standard photolithography equipment (365 nm). Two techniques have been performed and compared. The first technique is based on the preparation of self-assembled monolayers of photocleavable organosilane and the second one on photoassisted grafting (365 nm) of self-assembled monolayers (SAMs) on a silicon or glass substrate. In the first case, we begin with monolayers carrying an o-nitrobenzyl function (hydrophobic area) that is photochemically cleaved, revealing a carboxylic acid group (hydrophilic area). The problem is that the energy necessary to cleave this monolayer is too high and the reaction time is more than 1 h with 50 mW/cm(2) irradiation flux. To overcome this practical disadvantage, we propose another approach that is based on the thiol-ene reaction with benzophenone as photoinitiator. In this approach, a monolayer of mercaptopropyltrimethoxysilane (MPTS) is prepared first. Subsequently, a hydrocarbon chain is photografted locally onto the thiol layer, forming a hydrophobic surface while the reminding unmodified thiol surface is oxidized into sulfonic acid (hydrophilic area). We demonstrated the feasibility of this approach and synthesized high-quality self-assembled monolayers by UV grafting with an irradiation time of 30 s at 365 nm (50 mW/cm(2)). The modified surfaces have been characterized by contact angle measurements, X-ray photoelectron spectroscopy (XPS), AFM, and multiple internal reflection infrared spectroscopy (MIR-FTIR). The difference in the contact angles on the hydrophilic and hydrophobic surfaces reached a remarkable 77 degrees. We have also demonstrated that this method is compatible with selective surface grafting inside microfluidic channels.

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The sections in this article are Introduction Preparation and Immobilization of GPCRs on Functionalized Surfaces Signal Techniques Theoretical Approach The Impedance Network Model Equilibrium Fluctuations Conclusions Acknowledgments