Masked Multiplexed Separations to Enhance Duty Cycle for Structures for Lossless Ion Manipulations.
Masked Multiplexed Separations to Enhance Duty Cycle for Structures for Lossless Ion Manipulations.
复制标题
DOI:
10.1021/acs.analchem.0c04799
复制
发表时间:
2021-04-13
影响因子:
7.4
通讯作者:
DeBord JD
中科院分区:
文献类型:
--
作者:
Clowers BH;Cabrera E;Anderson G;Deng L;Moser K;Van Aken G;DeBord JD
The experimental paradigm of one ion packet release per spectrum severely hinders throughput in broadband ion mobility spectrometry systems (e.g. drift tube and traveling wave systems). Ion trapping marginally mitigates this problem, but the duty cycle deficit is amplified when moving to high resolution, long pathlength systems. As a consequence, new multiplexing strategies that maximize throughput while preserving peak fidelity are essential for high resolution IMS separations (e.g. SLIM and multi-pass technologies). Currently, broadly applicable deconvolution strategies for Hadamard-based ion multiplexing are limited to a narrow range of modulation sequences and do not fully maximize the ion signal generated during separation across an extended path length. Compared to prior Hadamard deconvolution errors that rely upon peak picking or discrete error classification, the masked deconvolution matrix technique exploits the knowledge that Hadamard transform artifacts are reflected about the central, primary signal (i.e. the true arrival time distribution (ATD)). By randomly inducing mathematical artifacts it is possible to identify spectral artifacts simply by their high degree of variability relative to the core ATD. It is important to note that the deweighting approach using the masked deconvolution matrix does not make any assumptions about the underlying transform and is applicable to any multiplexing strategy employing binary sequences. In addition to demonstrating a 100-fold increase in the total number of ions detected, the effective deconvolution of data from 5, 6, 7, and 8-bit pseudo-random sequences expands the utility and efficiency of the SLIM platform.
登录
查看更多内容
影响因子:
7.4
作者:
Poltash ML;McCabe JW;Shirzadeh M;Laganowsky A;Clowers BH;Russell DH
通讯作者:
Russell DH
影响因子:
7.4
作者:
KNORR, FJ;EATHERTON, RL;HILL, HH
通讯作者:
HILL, HH
影响因子:
3.5
作者:
Hanley, QS
通讯作者:
Hanley, QS
DOI:
10.1039/c7an00031f
发表时间:
2017-03-27
期刊:
The Analyst
影响因子:
--
作者:
Ibrahim YM;Hamid AM;Deng L;Garimella SV;Webb IK;Baker ES;Smith RD
通讯作者:
Smith RD
影响因子:
7.4
作者:
Li, Ailin;Nagy, Gabe;Conant, Christopher R.;Norheim, Randolph, V;Lee, Joon Yong;Giberson, Cameron;Hollerbach, Adam L.;Prabhakaran, Venkateshkumar;Attah, Isaac K.;Chouinard, Christopher D.;Prabhakaran, Aneesh;Smith, Richard D.;Ibrahim, Yehia M.;Garimella, Sandilya V. B.
通讯作者:
Garimella, Sandilya V. B.