2019
DOI: 10.1016/j.bios.2018.10.052
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Multichannel digital heteronuclear magnetic resonance biosensor

Abstract: Low-field, mobile NMR systems are increasingly used across diverse fields, including medical diagnostics, food quality control, and forensics. The throughput and functionality of these systems, however, are limited due to their conventional single-channel detection: one NMR probe exclusively uses an NMR console at any given time. Under this design, multi-channel detection could only be accomplished by either serially accessing individual probes or stacking up multiple copies of NMR electronics; this approach s… Show more

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Cited by 27 publications
(10 citation statements)
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“…The digital pulse programmer is powered by an ultra-low-cost microcontroller (e.g., Arduino). Multichannel parallelization of heteronuclear magnetic resonance sensors was also demonstrated by Huber et al [31].…”
mentioning
confidence: 84%
“…The digital pulse programmer is powered by an ultra-low-cost microcontroller (e.g., Arduino). Multichannel parallelization of heteronuclear magnetic resonance sensors was also demonstrated by Huber et al [31].…”
mentioning
confidence: 84%
“…To detect prostate-specific antigen, nitrocellulose membrane-based test strips were designed and measured by a portable NMR relaxometer [136]. Many other research groups attempted to adopt this technique to detect several biological components such as food borne bacteria [137][138][139][140] (See Figure 6).…”
Section: Nmr-based Bioassaymentioning
confidence: 99%
“…The scientists from all over the world combine chemistry, biology, physics, electronics, material science, biotechnology and nanotechnology to follow recent developments due to their specific properties of sensing abilities (Artika et al, 2020; Bhalla et al, 2020; Chakhalian et al, 2020; Mehta et al, 2020; Qin et al, 2020). A biosensor is an analytical tool that integrates a sensing part with a physical transducer including electrochemical (Pacheco et al, 2018), optical (Erdem et al, 2019), piezoelectric (Dagdeviren et al, 2015), thermal (Wang et al, 2019), magnetic (Huber et al, 2019) and so on (Scheme 1).…”
Section: Introductionmentioning
confidence: 99%