Sensors & Actuators: B. Chemical

Using a self-developed automatic sensor system with novel signal decomposition, we successfully partitioned CO2 flux into two independent streams derived from the two distinct pools in maize silage, following two independent processes: a physical venting of stored gas through a tortuous diffusive pathway, and a biochemical process generating gas in real time. Three silage samples, treated with a chemical or a biological additive, or left untreated, were tested. The signal decomposition found two best-fit functions (0.8034 ≤ R2 ≤ 0.9036), a quadratic CO2 discharge function and an exponential CO2 production function, for characterizing these distinct processes.

This data was obtained from the research titled "A Comparative Study of Non-Enzymatic Glucose Detection in Artificial Human Urine and Human Urine Specimens by using Mesoporous Bimetallic Cobalt-Iron Supported N-doped Graphene Biosensor based on Differential Pulse Voltammetry " to quantify the CoFe catalysts.

SF@CuNCs detetion S2-

This dataset is originated from 12 types of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors during 2220 minutes. The dataset is formatted in "xlsx" file. Each sheet represents one beef cut which is contained columns as follows: Minute: time in minute TVC: continuous label in the total viable count Label: discrete label, 1,2,3,4 denote “excellent”,”good”,”acceptable”, and “spoiled”, respectively. MQ_: the resistant value of gas sensors. In addition, the file "partition.zip" contains a dataset partition for training (50%), testing (25%), and validation data (25%). The first column shows the class label and the rest are features.

This program (main.py, OpenCV, written by Python) provides the XY-position of particles in each frame of the original movie (255.m4v) and a movie with circles indicating the particle positions (255_result.mp4). For the analysis of particle displacement, we used the distribution of brightness around the target particle as a template, where the area of the particle in a movie image is brighter than the other area (255_result.txt). The detected XY-position of the template was determined as the XY-position of the particle in the frame.

Data set for publication in Sensors and Actuators B: Chemical.

Hygromorphic actuation of dielectric elastomers

This program (detectWall.py, OpenCV, written by Python) provides the X-position of the pier from the original movie (230.m4v) and a movie with detected lines indicating the pier edges (230_20190207191754_wall.mp4). For the analysis of pier displacement, we used the feature of pier in the movie data, where the area of pier in a movie image is brighter than the other area. First, the template of brightness corresponding to the pier and non-pier areas was prepared, and then, the template matching was performed in each frame of the movie (230_20190207191754.all.txt, 230_20190207191754.wall.txt). The detected X-position of the template was determined as the X-position of the pier in the frame.

Relative changes of DC resistances of WO3 doped gas sensing layers. This data were used in the joined paper. Values of DC resistances were included in the figures of the paper.

Original data for publication.