robotic-based-experimental-procedure-for-colorimetric-gas-sensing

Robotic-based Experimental Procedure for Colorimetric Gas Sensing Development

This paper presents a robot-based experimental program aimed at developing an efficient and fast colorimetric gas sensor. The program employs an automated ...

Wenzhou University

This paper introduces a novel multivariate regression approach based on a multiple fitting algorithm that combines fitting functions to accordingly configure different regression models for the quantitative analysis of spectra data. The novel regression method employs multivariate fitting functions to characterize the potential functional relationship of spectral information and sample information with the corresponding attributes, and uses single fitting function elements as the independent variables for biased parameters to determine the amplitude of each fitting function. The peak width of the different fitting functions and the biased parameters are optimized by a simulated annealing algorithm. After parameter optimization, the fitting functions are superposed together to form a functional super surface, and a multiple fitting regression model is then used to characterize any functional relationship among the spectral variable information, sample information, and the corresponding analyte information. In this paper, a common fitting function, namely the Gaussian function, is used to create multiple fitting regression models. The simulated dataset and two real near infrared spectral datasets were used to validate the multiple fitting regression model. The results are compared to those obtained using partial least squares regression and least squares support vector regression. It is shown that the proposed multiple fitting regression algorithm achieved an accuracy comparable to the two conventional methods. Therefore, the multiple fitting regression is demonstrated to be a useful tool for spectra multivariate regression analysis and may also be suitable for linear and nonlinear multivariate calibration.

A novel spectral multivariate calibration approach based on a multiple fitting method.

 samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of  could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments.

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa.

First Draft Genome Assembly of the Seaweed .

 is an important economic seaweed in East Asia. In this study, we characterized the complete chloroplast genome sequence of  using PacBio long-read sequencing technology. It had a circular mapping molecular with the length of 124,286 bp, with a large single-copy region (LSC, 73,437 bp) and a small single copy region (SSC, 40,131 bp) separated by a pair of inverted repeats (IRs, 5,359 bp). The cp genome contained 173 genes including 139 protein-coding, 6 rRNA, and 28 tRNA genes. The phylogenomic analysis indicated that  is closely related to .

Characterization of the complete chloroplast genome of  and its phylogenomic position within phaeophyceae.

Three-input logic primitives show high efficiency in logic synthesis compared to traditional two-input logic, which encourages researchers to implement three-input logic gates with emerging nanotechnologies. This paper demonstrates a compact implementation of three-input monotone logic gates based on the inverted T-shaped TFET. Firstly, based on the gate coupling mechanism in the novel inverted T channel, the BTBT current can be suppressed in the vertical or horizontal region to achieve the channel strobe. Therefore, the typical three-input monotone logic functions, Majority, OrAnd, and AndOr, are successfully implemented on a single transistor. Then, a simplified potential model describing gate coupling is established to describe the impact of key device parameters on the logic behavior. Combined with TCAD simulation, the design rules of devices with different logic functions are given. Finally, a series of three-input monotonic logic gates are designed and verified. The results show that the use of the proposed TFETs can effectively save the number of transistors in the three-input logic gate, which indicates that the three-input TFET is a compact and flexible candidate for three-input logic gates.

Yijian Shi

College of Electrical and Electronic Engineering

Wenzhou University

Robotic-based Experimental Procedure for Colorimetric Gas Sensing Development

Yijian Shi

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}College of Electrical and Electronic Engineering

Wenzhou University

Robotic-based Experimental Procedure for Colorimetric Gas Sensing Development

College of Electrical and Electronic Engineering