A Floating Capsule Electrochemical System for In Situ and Multichannel Ion-Selective Sensing.

Free-floating electrochemical sensors are promising for in situ bioprocess monitoring with the advantages of movability, a lowered risk of contamination, and a simplified structure of the bioreactor. Although floating sensors were developed for the measurement of physical and chemical indicators such as temperature, velocity of flow, pH, and dissolved oxygen, it is the lack of available electrochemical sensors for the determination of the inorganic ions in bioreactors that has a significant influence on cell culture. In this study, a capsule-shaped electrochemical system (iCapsuleEC) is developed to monitor ions including K+, NH4+, Na+, Ca2+, and Mg2+ based on solid-contact ion-selective electrodes (SC-ISEs). It consists of a disposable electrochemical sensor and signal-processing device with features including multichannel measurement, self-calibration, and wireless data transmission. The capacities of the iCapsuleEC were demonstrated not only for in situ measurement of ion concentrations but also for the optimization of the sensing electrodes. We also explored the possibility of the system for use in detection in simulated cell culture media.

Optimization of Electrode Patterns for an ITO-Based Digital Microfluidic through the Finite Element Simulation.

Indium tin oxide (ITO)-based digital microfluidics (DMF) with unique optical and electrical properties are promising in the development of integrated, automatic and portable analytical systems. The fabrication technique using laser direct etching (LDE) on ITO glass has the advantages of being rapid, low cost and convenient. However, the fabrication resolution of LDE limits the minimum line width for patterns on ITO glasses, leading to a related wider lead wire for the actuating electrodes of DMF compared with photolithography. Therefore, the lead wire of electrodes could affect the droplet motion on the digital microfluidic chip due to the increased contact line with the droplet. Herein, we developed a finite element model of a DMF with improved efficiency to investigate the effect of the lead wire. An optimized electrode pattern was then designed based on a theoretical analysis and validated by a simulation, which significantly decreased the deformation of the droplets down to 0.012 mm. The performance of the optimized electrode was also verified in an experiment. The proposed simulation method could be further extended to other DMF systems or applications to provide an efficient approach for the design and optimization of DMF chips.

Colorimetric Sensing with Gold Nanoparticles on Electrowetting-Based Digital Microfluidics.

Digital microfluidic (DMF) has been a unique tool for manipulating micro-droplets with high flexibility and accuracy. To extend the application of DMF for automatic and in-site detection, it is promising to introduce colorimetric sensing based on gold nanoparticles (AuNPs), which have advantages including high sensitivity, label-free, biocompatibility, and easy surface modification. However, there is still a lack of studies for investigating the movement and stability of AuNPs for in-site detection on the electrowetting-based digital microfluidics. Herein, to demonstrate the ability of DMF for colorimetric sensing with AuNPs, we investigated the electrowetting property of the AuNPs droplets on the hydrophobic interface of the DMF chip and examined the stability of the AuNPs on DMF as well as the influence of evaporation to the colorimetric sensing. As a result, we found that the electrowetting of AuNPs fits to a modified Young-Lippmann equation, which suggests that a higher voltage is required to actuate AuNPs droplets compared with actuating water droplets. Moreover, the stability of AuNPs was maintained during the processing of electrowetting. We also proved that the evaporation of droplets has a limited influence on the detections that last several minutes. Finally, a model experiment for the detection of Hg2+ was carried out with similar results to the detections in bulk solution. The proposed method can be further extended to a wide range of AuNPs-based detection for label-free, automatic, and low-cost detection of small molecules, biomarkers, and metal ions.

Integrated Digital Microfluidic Platform for Colorimetric Sensing of Nitrite.

In this paper, a palm-size digital microfluidic (DMF) platform integrated with colorimetric analysis was developed for quantifying the concentration of nitrite. To realize the on-chip repeatable colorimetric analysis, a novel printed circuit board (PCB)-based DMF chip was designed with an embedded aperture on the actuator electrode, forming a vertical light path for online measurement of the droplets. The capabilities of the DMF platform enable automatic manipulation of microliter-level droplets to implement Griess assay without the use of external systems such as syringe, pump, or valve, which provides the benefits including high flexibility, portability, miniature size, and low cost. Results indicated the characteristics of good linearity (R 2 = 0.9974), the ignorable crosstalk for reusability, and the limit of detection (LOD) of nitrite as low as 5 µg/L. Furthermore, the presented platform was successfully applied to determine nitrite levels in food products with reliable results and satisfactory recoveries. This integrated DMF platform can be a promising new tool for a wide range of applications involving step-by-step solution mixing and optical detection in environmental monitoring, food safety analysis, and point-of-care testing.

Learning Shapelets for Improving Single-Molecule Nanopore Sensing.

The nanopore technique employs a nanoscale cavity to electrochemically confine individual molecules, achieving ultrasensitive single-molecule analysis based on evaluating the amplitude and duration of the ionic current. However, each nanopore sensing interface has its own intrinsic sensing ability, which does not always efficiently generate distinctive blockade currents for multiple analytes. Therefore, analytes that differ at only a single site often exhibit similar blockade currents or durations in nanopore experiments, which often produces serious overlap in the resulting statistical graphs. To improve the sensing ability of nanopores, herein we propose a novel shapelet-based machine learning approach to discriminate mixed analytes that exhibit nearly identical blockade current amplitudes and durations. DNA oligomers with a single-nucleotide difference, 5'-AAAA-3' and 5'-GAAA-3', are employed as model analytes that are difficult to identify in aerolysin nanopores at 100 mV. First, a set of the most informative and discriminative segments are learned from the time-series data set of blockade current signals using the learning time-series shapelets (LTS) algorithm. Then, the shapelet-transformed representation of the signals is obtained by calculating the minimum distance between the shapelets and the original signals. A simple logistic classifier is used to identify the two types of DNA oligomers in accordance with the corresponding shapelet-transformed representation. Finally, an evaluation is performed on the validation data set to show that our approach can achieve a high F1 score of 0.933. In comparison with the conventional statistical methods for the analysis of duration and residual current, the shapelet-transformed representation provides clearly discriminated distributions for multiple analytes. Taking advantage of the robust LTS algorithm, one could anticipate the real-time analysis of nanopore events for the direct identification and quantification of multiple biomolecules in a complex real sample (e.g., serum) without labels and time-consuming mutagenesis.

Single molecule study of initial structural features on the amyloidosis process.

We employed an α-hemolysin (α-HL) nanopore as a single-molecule tool to investigate the effects of initial structure on the amyloidosis process. The differences in the initial structure of two ß-amyloid (Aß) peptides (Aß25-35 and Aß35-25) could be distinguished in real-time due to their characteristic blockades. More importantly, the distinct aggregate dynamics for these two kinds of Aß fragments can be readily analyzed by monitoring the blockade frequency over time.