Study on the Chemical and Mechanical Stability of Polymer Nanofluidic Biosensors

Polymer nanofluidic devices have great potential to replace silicon (Si) and glass-based nanofluidic devices in biomedical applications due to their advantages such as low material and fabrication cost, various physicochemical properties, well-developed surface modification protocol, and low electrical noises for electrical measurements. In nanofluidic sensing applications, single molecules such as DNA are introduced into the fabricated nanochannel or nanopore, measuring their physicochemical properties optically or electrically. The properties of materials for nanofluidic devices have a significant role in the performance of the devices, such as DNA translocation and device stability. Among several nanoscale fluidic physics, surface charge density is a key material property of nanofluidic devices related to the capture of single molecules because it determines the magnitude of electrophoresis and electroosmosis in the nanostructures. To facilitate the capture of single molecules into nanofluidic devices, polymers containing poly(ethylene glycol) (PEG) are preferred due to their low surface charge density and reduction of surface fouling of biomolecules. However, a drawback of PEG-based polymers is a weak chemical and mechanical stability due to swelling effect and low surface hardness when in contact with electrolytes. This work presents an improvement in the chemical and mechanical stability of a nanofluidic device formed in poly(ethylene glycol) diacrylate (PEGDA), a PEG-based UV resin for UV-NIL, by adding a cross-linking agent (e.g. TMPTA). First, we defined the surface charge density of polymers such as PMMA, COC 6013, and PEGDA with the different O2 treatment time because these three polymers have low surface charge density compared to other polymers. Then, we studied the effect of the cross-linking agent content on the surface charge density of PEGDA-TMPTA material and on the translocation of DNA molecules through the nanopore. Five different compositions of PEGDA resins with varied amounts of a cross-linking 1 agent, trimethylolpropane triacrylate (TMPTA), were used (pure PEGDA, ratio 5:1, 1:1, 1:2, and 1:5). The surface hardness of PEGDA-TMPTA resin increases according to the crosslinking agent concentration from 139 MPa (pure PEGDA resin) to 205 MPa (1:5 resin). To be specific, the surface hardnesses of pure PEGDA, 5:1, 1:1, 1:2, and 1:5 were 139 MPa, 158 MPa, 196 GPa, 204 MPa, and 205 MPa, respectively. The surface charge densities at 0.001M KCl (pH 8.0) of pure PEGDA, 5:1, 2:1, 1:1, and 1:5 were −9.5 ± 0.09 / ! , −7.9 ± 0.97 / ! , −7.1 ± 1.06 / ! , −7.5 ± 1.10 / ! , and −7.4 ± 0.57 / ! , respectively. These observed surface charge densities of PEGDA-TMPTA resin exhibit a decreasing trend which is beneficial for DNA translocation into nanostructures. In conclusion, this approach has a positive influence on the chemical and mechanical stability of nanofluidic devices concerning DNA translocation into a nanopore or a nanochannel

A Package for the Automated Classification of Periodic Variable Stars

We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. In order to assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if the number of data points is larger than 80 and the duration is more than a few weeks. The classifier software of the subclass model is available from the GitHub repository (https://goo.gl/xmFO6Q).Comment: 16 pages, 11 figures, accepted for publication in A&

OBOE: Collaborative Filtering for AutoML Model Selection

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts. This paper introduces OBOE, a collaborative filtering method for time-constrained model selection and hyperparameter tuning. OBOE forms a matrix of the cross-validated errors of a large number of supervised learning models (algorithms together with hyperparameters) on a large number of datasets, and fits a low rank model to learn the low-dimensional feature vectors for the models and datasets that best predict the cross-validated errors. To find promising models for a new dataset, OBOE runs a set of fast but informative algorithms on the new dataset and uses their cross-validated errors to infer the feature vector for the new dataset. OBOE can find good models under constraints on the number of models fit or the total time budget. To this end, this paper develops a new heuristic for active learning in time-constrained matrix completion based on optimal experiment design. Our experiments demonstrate that OBOE delivers state-of-the-art performance faster than competing approaches on a test bed of supervised learning problems. Moreover, the success of the bilinear model used by OBOE suggests that AutoML may be simpler than was previously understood

Flexible Mold for Microstructures Replication

Space debris has been a growing concern in space exploration sector. To combat this issue, biomimicry is utilized to create a gecko’s feet microstructure that will be attached to a gripper or robotic arm. This will enable capture of debris through the use of dry adhesive microstructure. However, the production of such microstructures is expensive which hinders their implementation. The objective of this research is to develop an advanced fabrication process to mass produce gecko’s feet microstructure with soft polymer mold. The possibility of using different coating methods with coating materials will be justified. The process of fabricating mold and replicating mold will be optimized. The method of mass producing microstructures will be verified and the limitation of the method will also be studied