Influence of Oxygen Microenvironment on Microfluidic Glucose Sensor Performance

We propose a novel method to overcome significant problems of baseline drift and sensitivity degradation in amperometric biosensors based on oxidase enzyme reactions. A novel glucose microsensor with a built-in electrochemical oxygen manipulation microsystem is introduced to demonstrate three novel functionalities; one-point in situ self-calibration (zero-point), broadening of dynamic range and increase in sensitivity. The influence of electrochemically generated oxygen microenvironment on the sensor output within a fluidic structure is investigated

An Intelligent Dissolved Oxygen Microsensor System with Electrochemically Actuated Fluidics

A new dissolved oxygen monitoring microsystem is proposed to achieve in situ intelligent self-calibration by using an electrochemically actuated fluidic system. The electrochemical actuation, based on water electrolysis, plays two critical roles in the proposed microsystem. First, the electrochemically generated gases serve as the calibrants for the in situ 2-point calibration/diagnosis procedure of the microsensor in a chip. Secondly, the electrochemical generation and collapse of gas bubbles provide the driving force of the bidirectional fluidic manipulation for sampling and dispensing of the sample solution. A microsystem including a dissolved oxygen microprobe, electrochemical actuators, and a fluidic structure are prepared by microfabrication technology and its performance is characterized

Oxidase-Coupled Amperometric Glucose and Lactate Sensors with Integrated Electrochemical Actuation System

Unpredictable baseline drift and sensitivity degradation during continuous use are two of the most significant problems of biosensors including the amperometric glucose and lactate sensors. Therefore, the capability of on-demand in situ calibration/diagnosis of biochemical sensors is indispensable for reliable long-term monitoring with minimum attendance. Another limitation of oxidase enzyme-based biosensors is the dependence of enzyme activity on the background oxygen concentration in sample solution. In order to address these issues, the electrolytic generation of oxygen and hydrogen bubbles were utilized 1) to overcome the background oxygen dependence of glucose and lactate sensors and 2) to demonstrate the feasibility of in situ self-calibration of the proposed glucose and lactate sensors. Experimental data assure that the proposed techniques effectively establish the zero calibration value and significantly improve the measurement sensitivity and dynamic range in both glucose and lactate sensors

Glucose Oxidase (GOD)-Coupled Amperometric Microsensor with Integrated Electrochemical Actuation System

Recent developments for biosensors have been mainly focused on miniaturization and exploratory use of new materials. It should be emphasized that the absence of a novel in-situ self-calibration/diagnosis technique that is not connected to an external apparatus is a key obstacle to the realization of a biosensor for continuous use with minimum attendance. In order to address this issue, a novel solid-state glucose oxidase-coupled amperometric biosensor with integrated electrochemical actuation system has been designed and validated. There are two key components of the proposed glucose biosensor: solid-state GOD-coupled thin-lm amperometric sensing element and O2 depleting/saturating built-in electrochemical actuator. The actuator can be used to accomplish in-situ 1-point self-calibration by depleting O2 (i.e., by simulating glucose-free environment). Also, it can be used at the same time to extend the proposed sensor\u27s linear detection range by in ating O2 (i.e., by enhancing glucose sensitivity). A prototype sensor was fabricated and a series of lab experiments was conducted. Collected data assures that the proposed sensor effectively establishes the zero calibration point and signi cantly enhances its measurement sensitivity and con dence

Versatile Optochemical Quantification with Optical Mouse

There is an ever increasing need for simple, low-cost instruments for ubiquitous medical and environmental measurements in conjunction with networks and Internet-of-things. This work demonstrates that the optical mouse, one of the most common optoelectronic computer peripherals, can be used for chemical quantification. Particularly, we explore the feasibility of using the preassembled optical platform of mouse for oxygen and pH quantification. The image sensor and the light-emitting diode (LED) serve as photodetector and excitation/illumination light source, respectively, while the preinstalled microoptics (e.g., lens and waveguide) provide a fixed optical arrangement convenient for sample analysis. This novel, cost-effective approach demonstrates the potential application of optical mouse for bioanalytical devices in conjunction with commercial sensor strips or simple microfluidic elements. This is one viable option for seamless integration of bioanalytical capability into existing personal computers and associate networks without significant additional hardware

Photopatternable Polymeric Membranes for Optical Oxygen Sensors

A new class of optical oxygen sensor that can be photopatternable by traditional UV lithography is presented. They are fabricated using photopatternable spin-on silicone (polydimethylsiloxane, PDMS) with oxygen sensitive luminescent dyes. It has a good adhesion property and can be applied on glass or on photopolymer (SU-8) without any additional surface treatments. The optimum mixture composition for patternable oxygen sensitive membranes is investigated and its optical properties are characterized. Proof-of-concepts for two applications, intensity-based oxygen sensing with SU-8 based structure and self-calibration fluidic oxygen sensor, are described. These photopatternable optical membranes will find many applications wherever small patterns of oxygen sensitive membranes are required

BIOFUEL PRODUCTION FROM BIOMASS-DERIVED VOLATILE FATTY ACID PLATFORM

The typical biorefinery platforms are sugar, thermochemical (syngas), carbon-rich chains, and biogas platform. The sugar platform uses hexose and pentose sugars extracted or converted from plant body. The thermochemical (syngas) platform is chemical or biological conversion process using pyrolysis or gasification of plant to produce biofuels. The carbon-rich chains platform is used to produce biodiesel from long-chain fatty acids or glycerides. Those platforms have unique advantages and disadvantages. Our group has concentrated on the biogas platform producing methane gas from municipal solid wastes through anaerobic digestion (AD) processs, which is composed of rapid acidogenesis and slow methanogenesis. This acidogenic and methanogenic process is widely used for biogas production form the treatment of wetted waste materials (foodwastes, sludge, and manure) in the worldwide. The volatile fatty acids (VFAs) are short-chain fatty acids composed of mainly acetate and butyrate, and easily produced from non-woody biomass with low lignin content in acidogenesis step by the natural consortia of mixed anaerobic bacteria. And then it is slowly converted to biogas (methane, CO2) by methanogenic bacteria naturally. Now, we would like to suggest a new platform using VFAs for biofuel and biochemicals production, because the VFAs can be produced form a cost-effective way using AD process that does not need sterilization, additional hydrolysis enzymes (cellulase and xylanase) and high cost pretreatment step in case of low-lignin content biomass. Considering that raw material alone constitutes 60-80% of biofuel production costs, biofuels made from the VFAs derived from the waste organic biomass can have a potential of economical advantage. A problem is how to convert VFAs to biofuels and biochemicals. In the presentation, we will give possible solutions in order to produce bioethanol, biobutanol, biodiesel, and biohydrogen as well as biogas through biological or chemical processes. And we will introduce our ongoing researches related with the VFA platfor

Automated Oxidase-Coupled Amperometric Microsensor with Integrated Electrochemical Actuation System for Continuous Sensing of Saccharoids

Recent developments for biosensors have been mainly focused on miniaturization and exploratory use of new materials. It should be emphasized that the absence of a novel in-situ self-calibration/diagnosis technique that is not connected to an external apparatus is a key obstacle to the realization of a biosensor for continuous use with minimum attendance. To address this deficiency, a novel needle-type biosensor system with fully automated operations is being developed, in which a novel oxidase-coupled amperometric sensor with oxygen depleting/generating actuator is interfaced with an electrochemical instrument and a perfusion system. Labview virtual instrument has been also developed to oversee the automatic control of the prototype sensor. Using the proposed system, a large amount of data can be rapidly collected for more effective sensor characterization and more advanced sensor designs. Autonomous and continuous sensing and self-calibration with minimal human intervention is also envisioned

VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning

Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes' rule, that can naturally incorporate class imbalance into the Active Learning framework. We derive that three terms should be considered together when estimating the probability of a classifier making a mistake for a given sample; i) probability of mislabelling a class, ii) likelihood of the data given a predicted class, and iii) the prior probability on the abundance of a predicted class. Implementing these terms requires a generative model and an intractable likelihood estimation. Therefore, we train a Variational Auto Encoder (VAE) for this purpose. To further tie the VAE with the classifier and facilitate VAE training, we use the classifiers' deep feature representations as input to the VAE. By considering all three probabilities, among them especially the data imbalance, we can substantially improve the potential of existing methods under limited data budget. We show that our method can be applied to classification tasks on multiple different datasets -- including one that is a real-world dataset with heavy data imbalance -- significantly outperforming the state of the art