Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Midinfrared (2.5-25 μm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1021/acssensors.4c01220 | DOI Listing |
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