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    <identifier>10.57760/sciencedb.07353</identifier>
    <datestamp>2023-02-13T14:55:04Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2023-02-13</dc:date>
  <dc:title>Normal-inverse bimodule operation Hadamard transform ion mobility spectrometry</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.07353</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>In order to suppress or eliminate the spurious peaks and improve signal-to-noise ratio (SNR) of Hadamard transform ion mobility spectrometry (HT-IMS), a normal-inverse bimodule operation Hadamardtransform - ion mobility spectrometry (NIBOHT-IMS) technique was developed. In this novel technique, anormal and inverse pseudo random binary sequence (PRBS) was produced in sequential order by an iongate controller and utilized to control the ion gate of IMS, and then the normal HT-IMS mobility spectrumand the inverse HT-IMS mobility spectrum were obtained. A NIBOHT-IMS mobility spectrum was gainedby subtracting the inverse HT-IMS mobility spectrum from normal HT-IMS mobility spectrum. Experimental results demonstrate that the NIBOHT-IMS technique can signifificantly suppress or eliminate thespurious peaks, and enhance the SNR by measuring the reactant ions. Furthermore, the gas CHCl3 andCH2Br2 were measured for evaluating the capability of detecting real sample. The results show that theNIBOHT-IMS technique is able to eliminate the spurious peaks and improve the SNR notably not only forthe detection of larger ion signals but also for the detection of small ion signals.</dc:description>
  <dc:subject>Ion mobility spectrometry; Hadamard transform; Signal-to-noise ratio; Spurious peaks</dc:subject>
  <dc:creator>Yan Hong</dc:creator>
  <dc:creator>Chaoqun Huang</dc:creator>
  <dc:creator>Sheng Liu</dc:creator>
  <dc:creator>Lei Xia</dc:creator>
  <dc:creator>Chengyin Shen</dc:creator>
  <dc:creator>Yannan Chu</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1016/j.aca.2018.05.004</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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