Fabrication of Gold Nanodot Array for the Localized Surface Plasmon Resonance

Localized surface plasmon resonance (LSPR) is a promising method for detecting antigen-antibody binding in label-free biosensors. In this study, the fabrication of a LSPR substrate with a gold nanodot array through the lift-off process of an alumina mask is reported. The substrate showed an extinction peak in its extinction spectrum, and the peak position was dependent on the height of the gold nanodot array, and the change of extinction peak with the height could be predicted by the numerical simulation. In addition, the peak position was observed to be red-shifted with the increasing RIU value of the medium surrounding the gold nanodot array. In particular, the peak position in the 10 nm thick gold nanodot array was approximately 710 nm in air, and the sensitivity, defined as the ratio of the shift of peak position to the RIU of the medium, was 323.6 nm/RIU. The fabrication procedure could be applied to fabricate the LSPR substrates with a large area

Hydrogel-Based Capillary Flow Pumping In A Hydrophobic Microfluidic Channel

In this study, we propose a novel method to generate a capillary pressure-driven flow in a microchannel with a hydrophobic surface. The microfluidic device has a wide channel in which a hydrogel pillar array is embedded. The hydrogel pillar array was formed in the microchannel by a photopolymerization process. The flow rate due to a capillary action was strongly dependent on the distance between the pillars. Moreover, our capillary pumping with a hydrogel pillar array sustained the flow for more than 5 min with a limited sample volume. Our microfluidic device provides two advantages: (1) the modification of the polymer surface to make it hydrophilic is not required and (2) the conventional polymer molding technique can be applied to produce microfluidic devices, instead of the precision molding technique. The results indicate the possible fabrication of various microfluidic chip devices that can be easily implemented in point-of-care diagnostics. © 2014 The Japan Society of Applied Physics

Detection of Biomarkers Using LSPR Substrate with Gold Nanoparticle Array

In the biosensing platform, label-free detection technique provides advantages such as the short analysis time and the cost-effectiveness. In this study, we showed the feasibility of the LSPR substrate with gold nanoparticle array for detecting low density lipoprotein (LDL) and high density lipoprotein (HDL) without labeling. The LSPR substrate was fabricated through the lift-off process with the anodized alumina mask, and its LSPR phenomenon was observed by measuring the optical transmission of substrate. The antibodies were immobilized on the gold nanoparticle array via the chemical binding, in which the 11-MUA was used as the linker to bind the antibodies. The binding of antibodies was confirmed by observing the shift of LSPR peak of the substrate. Finally, with the LSPR substrates with the antibodies immobilized, the detection of LDL and HDL was investigated. As a result, LDL and HDL could be detected in the clinically available concentration range, respectively

Deep Learning Diffuse Optical Tomography

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent